Peter - Cracking Markets Profile picture
Feb 3 7 tweets 3 min read Read on X
Terrified of buying the top in Gold, Bitcoin, or the Nasdaq? But afraid of missing the next leg up?

You aren't alone. This is the "Trader's Dilemma."

The solution isn't a crystal ball. It is a system.

Here is a robust framework catching "fat tails" while keeping Max Drawdown to just -15%.

Full logic below 🧵Image
1/ The Philosophy

We are trading a classic Trend Following architecture. Assets: Gold ($GC), Nasdaq ($NQ), Micro Bitcoin ($MBT).

Why these three?
- High Volatility
- Hard Trends
- Low Correlation

We don't guess when they move. We set a trap and wait for price to trigger it.
2/ The Setup

Simple systems are robust systems. This approach is time-tested.

We combine a Donchian Channel (Trend) with a Chandelier Stop (Risk).

The Rules:
• Timeframe: Daily
• Trend Filter: 20-Day Donchian
• Volatility: 5-Day ATR

ENTRY (The Breakout): IF Close > Highest High (20 days) THEN Buy Long.Image
3/ Risk Management

This is where you survive.

POSITION SIZING (Volatility Targeting): We risk 2% of equity per trade.
• Stop Distance = 3 * ATR(5)
• Size = (Account * 0.02) / Stop Distance

The Insight: If Volatility (ATR) goes UP, our Position Size goes DOWN. We equalise risk across Bitcoin and Gold.
4/ The Exit (Two-Stage Mechanism)

We use two exits:

A. Protective Stop (The Shield): Trail a stop at Highest High - (3 * ATR). Saves us if a breakout fails instantly.

B. Technical Exit (The Signal): Sell if Close < Lowest Low (20 days). Confirms the trend is actually over. Image
5/ The Hardest Part: Psychology

Look at the stats:
• Win Rate: 47.3%
• Avg Win: 4.30%
• Avg Loss: 2.06%

We are wrong more often than right.

But trading a system is psychologically easier than guessing tops and bottoms. You stop fighting the market and start executing the plan.Image
6/ Next Steps

Stop trying to time the exact bottom. You will miss. Start measuring the breakout.

Systematic execution > Emotional guessing.

Note: This is a foundational framework, not a "black box" solution. But it beats discretionary guessing every time.

Tools used: RealTest & Norgate Data.

If you found value in this breakdown:
- Retweet the first tweet to share the alpha.
- Follow me for more systematic trading deep dives.

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More from @SystematicPeter

Jan 5
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. 🧵 The chart displays the theoretical potential (gross profit) of the breakout principle itself without applying costs and risk management.
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.Gold futures 120-min candlestick chart showing rising trend with marked swing highs (blue) and swing lows (red) around 4000–4400 price levels.
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?
Read 8 tweets
Dec 8, 2025
In January 2025, I published a mean reversion strategy with a free backtester.

11 months later, here are the out of sample results:

- +30.4% return (vs +24.4% Nasdaq)
- -10.2% max drawdown (vs -22.8% Nasdaq)
- 72% win rate
- 83 trades

Here's the exact system: Image
The idea is simple:

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. Image
Read 10 tweets
Oct 2, 2025
Leveraged ETFs can be a great source of alpha — if you treat them as mean-reverting wrappers 📉📈 rather than long-term investments.

Here’s a simple $TQQQ / $QQQ swing strategy that beat buy & hold with only 24% average capital use 👇 Image
Leveraged ETFs (like $TQQQ, 3× $QQQ) don’t perfectly track their target.

⚡ Daily compounding
⚡ Volatility drag
⚡ Rebalancing frictions

Result: “Excess decay” appears when TQQQ underperforms its ideal 3× of QQQ over short horizons. That excess tends to mean revert.
We quantify the excess:

ExcessN = log-return(TQQQ,N) – 3 × log-return(QQQ,N)

Then normalize with a Z-score → ExcessZ.

📥 Entry: on close when ExcessZ ≤ –1.0 (TQQQ too cheap vs 3× QQQ)
📤 Exit: on close when ExcessZ ≥ 0, after 5 bars, or if ExcessZ ≤ –3.0 (fail-fast).
Read 6 tweets
Jul 24, 2025
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. 🧵👇Image
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.
Read 6 tweets
Jul 2, 2025
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. Image
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
Read 11 tweets
May 5, 2025
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.Image
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.
Read 5 tweets

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