u/Ok_Can_5882

A trading Youtuber called The Trading Geek claims he turned $20 into $10M daytrading using smart money concepts (liquidity sweeps, order blocks, fair value gaps). Videos like his get millions of views, and viewers walk away with a strategy they think they can trade — but almost none of these creators show a real backtest, just a curated equity curve or a handful of cherry-picked chart examples. So to check if his strategy actually holds water, I coded it from scratch and ran 130,201 parameter combinations against 5 years of real market data.

The strategy

Three building blocks, chained together:

1. Liquidity sweep — price runs past a recent high/low, traps breakout traders, then reverses (break of structure)
2. Order block — the last candle before that reversal, where he claims institutional money entered
3. Fair value gap — an imbalance the market tends to fill; used for entry timing instead of blindly entering at the order block

The problem with coding and backtesting this strategy is that it's not fully mechanically defined. There are no fixed rules for what counts as a valid swing point, how deep a sweep needs to be, or how big a fair value gap should be. That ambiguity is exactly why nobody backtests these strategies properly. Which is likely intentional. Instead of picking one interpretation, I turned every vague concept into parameterized rules and tested every combination: swing point detection, liquidity sweep depth, fair value gap size/location, entry timing, timeframe, asset, etc.

Results

The baseline version, or as close as I could get to it with mechanical rules, lost 60% over 5 years even with the best possible settings (realistic trading costs included).

Out of all 130,201 configurations, 24,236 were profitable. Only 211 beat the market. Digging into what those 211 have in common:

• Price-based swing detection (directional change method) beats time-based, and smaller swings outperform bigger ones
• Shallow liquidity sweeps outperform deep ones — large sweeps produced zero winners
• Fair value gaps actually hurt performance — the best entry model skips FVGs entirely and enters at the order block midpoint instead, contradicting his own claim
• Lower reward-to-risk is best; 1:1.5 was most prevalent
• Almost 70% of winners cluster on the 1H timeframe, despite The Trading Geek's claim that it ought to work on any timeframe.

With every optimized parameter locked in, average return across the winners was 159% over 5 years, and the config held up (profitable) on 6 of 7 assets tested — a full run came to roughly +2,500%. But just to be clear, that number is very likely inflated by selection bias. Testing 130k variations and cherry-picking the best one is a classic overfitting trap. Whether this specific configuration has a real edge or is just noise is a separate question.

I'm sharing this as a reminder to never blindly trust anything you see online, especially when it comes to trading. It's much easier to sell courses than it is to make money in the markets.

If you want to see full breakdown of the strategy and results, you can find it in this video, or check out the code on my github page. If you disagree with how I mechanized any part of the strategy, the definitions are all there and you can test your own version.

When I started trading almost a decade ago, I was a purely manual, discretionary, and unprofitable trader. I never found consistent success trading manually for many reasons, but one of the things I kept running into was that I was basically guessing market structure. It's often not obvious which highs 'count' and which ones don't, and that uncertainty completely undermined my trading.

I only became profitable years later, once I made the switch to algo trading. Some of my systems still lean on market structure concepts, but now everything runs on purely objective, code-based methods instead of eyeballing a chart. Thought this might be useful for other traders, so I'm sharing it here.

I rely mainly on 4 algorithms for market structure detection:

• Directional Change — confirms a swing once price reverses by a set threshold
• Rolling Window — checks if a bar is the highest/lowest within a fixed window on either side
• Perceptually Important Points — iteratively finds the points that visually stand out most (mimics human perception)
• Persistence-Based Detection — ranks swings by how significant they are, using a "flooding" model where the longest-surviving peaks and valleys are the most meaningful

Each method has different tradeoffs, and I use each one for different purposes. If this interests you, I made a short youtube video explaining each algo in more detail, and all the code is listed on my github page. All free, not trying to sell anything.

Curious to hear how y'all are analyzing market structure.

When I first started trading, I bought a book from a Trading World Cup champion called Robert Miner. The only reason I bought it is because of that world cup credential. And after reading the book, I spent months backtesting the strategies and ideas from the book. But none of the strategies worked at all...not even close. And that's when I started questioning the legitimacy of this 'world cup'.

After doing some research, I am now certain that you can 'win' this championship without actually knowing anything about trading. The problem is that competitors can use as many accounts as they want, and nobody keeps track. So if you have enough money to keep opening accounts, you're basically guaranteed to get one of the accounts to 2.5x, which is usually enough to earn first place.

I calculated that you can guarantee a 95% chance of winning first place with just 9 accounts, or a total net cost of about $21k. In one of the smaller events, a place on the leaderboard is basically guaranteed for only $2k. That sounds like a lot of money for a trophy, but the return on investment is huge if you have bad intentions.

Basically all the world cup 'champions' are selling paid services, and use their trophy as proof of their trading abilities. Think of Fabio Valentini, Andrea Unger, Robert Miner, etc. And all of those guys are making millions from selling courses and mentorships. I obviously can't prove that they're all scammers, but I can prove that winning the Trading World Cup says absolutely nothing about someone's true trading ability. If you want to see the proof, you can check out this youtube video :) and all the code I used is on my github page.

TLDR; don't trust the Trading World Cup, it tells you nothing about someone's trading ability.