Rejected methods on FX Backtest Diary

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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:

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.

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 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.

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!

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.

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.

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.

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.

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).

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:

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?

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.

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.

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).