Most traders fade resistance. They bet on the reversal.
I ran a raw probability test across 40 futures markets over 25 years - 40,000+ breakout events.
The data is clear: fading strong swings is usually the wrong bet.
Here is the truth about Swing Breakouts. 🧵
1/ What is a "Swing"?
A swing is a prior pivot high/low that held for a while - a level the market respected.
In this test:
- Swing = the most recent significant daily pivot high/low
- “Significant” = at least ~1x ATR separation vs the prior pivot (to avoid noise)
It is not just a line. It is a liquidity pocket.
2/ The Experiment
Goal: isolate the signal BEFORE filters and optimization.
- Markets: 40 futures (indices, commodities, bonds)
- Entry: first break of the prior significant daily swing
- Exit: time-based, hold 1 hour
- Optimization: none (same rules across markets)
- Costs: none (raw probability test)
Question: after the break, does price continue or snap back?
Buy stocks that drop sharply - but only if they're still in an uptrend.
Short-term panic creates opportunity.
Long-term trend provides the safety net.
This is mean reversion 101.
Entry rules:
1. Stock is above its 200-day MA (uptrend filter) 2. Stock drops more than 3% in one day (panic signal) 3. Next day: place limit buy at 0.9 × ATR(5) below close
You're buying the dip - then waiting for an even cheaper price.
1/6 The Power of "Boring": How Low Volatility Supercharges Mean Reversion
Mean reversion is a popular concept, but it doesn't work the same on all stocks.
I backtested the exact same system on two different baskets of stocks. The difference in performance is stunning. Here's the one factor that changes everything. 🧵👇
2/6 The system is the same - a long-only, mean-reversion strategy on S&P 500 stocks:
🟩 Green Line = Prioritizes LOW-volatility stocks
🟥 Red Line = Prioritizes HIGH-volatility stocks
The chart speaks for itself. Low volatility is smoother, safer, and vastly more profitable.
3/6 So, why does "boring" win?
Mean reversion needs a reliable "mean" to revert to.
Low-volatility stocks are stable. Price drops are often overreactions, making a snap-back likely.
High-volatility stocks are unpredictable. A sharp drop might be a "falling knife," not a bargain.
Getting more and more tradable systems designed by LLMs.
This is what I’ve learned actually works for me.
👇 Step-by-step breakdown of the process that turns AI ideas into real systems I can paper trade.
1/ I start in ChatGPT (o3 model).
I use a custom bio where I describe myself as an experienced trader—this primes it into “expert mode.”
Then I share a picture from a real trading session and describe the context.
In this case: intraday breakout from a volatility “squeeze.”
I explain the type of data I have + what kind of realistic performance I expect (after slippage & fees).
Then I ask GPT to brainstorm and rank strategies by:
- difficulty to implement
- likelihood of working live
2/ That prompt usually delivers surprisingly good ideas.
I pick the one that makes the most sense intuitively.
Then I ask GPT to:
- write a full, detailed trading plan
- create step-by-step implementation instructions for a programmer to code it into a backtester
1/5 Let's talk about something crucial but often overlooked in deep research, especially in systematic trading: Note-Taking. 📝
When you're diving into years of complex research, building strategies, and testing hypotheses, how you organize your knowledge is paramount. Without structure, you'll inevitably find yourself going in circles, re-discovering old insights, or losing valuable connections.
It's a huge, hidden productivity drain. You need more than just scattered files; you need a second brain.
2/5 My personal game-changer?
The Zettelkasten Method, implemented using Obsidian (@obsdmd). 🧠 Obsidian is free and works directly with plain Markdown (.md) files stored locally on your computer. This means:
✅ You truly OWN your data.
✅ No internet required – full offline access.
✅ Notes are simple text files, readable by anything, forever.
This isn't just storage; it's about connecting notes into a powerful knowledge network – a 'second brain'. Optional sync keeps it available across devices if needed.
3/5 The real power boost within Obsidian comes from the Dataview plugin. Think of it as a dynamic query engine layered on top of your notes. 🚀 By adding simple metadata (like status: backtesting, asset_class: equities, strategy_type: mean_reversion, priority: high) I can create live dashboards and lists. Imagine instantly querying:
- All trading systems currently being researched.
- Ideas that haven't been tested yet.
- Notes related to a specific paper or dataset that have backtest etc.
It transforms static notes into an interactive research database.