@Michael__Invest
Account based inGermany
About this account
- Account based in
- Germany
- Connected via
- Germany App Store
Account-level information from X, not a live location or the device used for a specific post.
Joined December 2019
- Tweets3.8K
- Following173
- Followers115
- Likes5.1K
Michael retweeted
What a month for my intraday volatility breakout system.
Finally, a new equity high for the version I share on my website.
This is what trading a breakout system can look like: plenty of losing trades, stretches of going nowhere, then a few big winners that move the equity curve.
That is one reason I like to automate it.
The last thing I want is to sit through the losses, lose patience, and miss the trades that make the difference.
I track the out-of-sample results publicly, so you can see the frustrating periods as well as the new highs:
crackingmarkets.com/intraday…
Michael retweeted
Breakouts around the open can be very profitable.
I tested this simple NQ model from 2018–2026.
$100k → $3.77M, 52% CAGR, 1.40 Sharpe, -35% max DD
QQQ over the same period:
19% CAGR, 0.84 Sharpe, -36% max DD
MNQ commissions + slippage are included.
And here are the full rules:
1. Entries
TR1 = true range of the previous full session.
Long: Open + 0.25 × TR1
Short: Open - 0.25 × TR1
Orders active 09:30 - 13:00 ET.
2. Stop
0.25 × TR1 from entry = 1R.
3. Break-even
After a completed bar reaches +0.50 × TR1 = +2R, move the stop to entry from the next bar.
4. Exit
Stop, break-even, or market exit at 15:55 ET.
No profit target, no trailing stop.
5. Re-entry
Re-entry is allowed after a stop-out once price trades back through the entry level from the other side.
Maximum 3 entries per day across long + short.
6. Sizing
Risk 1% of current equity per trade using MNQ.
Equity compounds from $100k.
7. Filters
None.
Costs
$0.95 round-trip commission per MNQ + 1 tick slippage per side on every fill.
One important observation:
The strategy has performed much better during the higher-volatility years. In quieter periods, failed breakouts and trading costs create much more drag.
That is worth investigating further.
But as a simple, symmetric breakout with essentially no filters, I find the result very interesting.
Full rules above. Test it yourself.
Michael retweeted
Your research is correct. We came to that conclusion back in around 2000/01.
High capitalised markets such as the US are no good for absolute trend following. Stick with momentum. Lower capitalised markets such as Australia and Canada DO benefit from absolute trend following as well as momentum.
Michael retweeted
Originally I come from the trend following and momentum side, and both are still an essential part of my strategy portfolio.
The interesting part: trend following 𝗱𝗼𝗲𝘀 𝗻𝗼𝘁 𝘄𝗼𝗿𝗸 𝘁𝗵𝗮𝘁 𝘄𝗲𝗹𝗹 𝗼𝗻 𝘁𝗵𝗲 𝗨𝗦 𝗺𝗮𝗿𝗸𝗲𝘁. At least according to a research paper I studied a while ago — and my own analyses clearly confirmed it. Trend following works much better on the 𝗔𝘂𝘀𝘁𝗿𝗮𝗹𝗶𝗮𝗻 market, the 𝗖𝗮𝗻𝗮𝗱𝗶𝗮𝗻 market or the 𝗦𝗰𝗮𝗻𝗱𝗶𝗻𝗮𝘃𝗶𝗮𝗻 markets.
So here is a position from my trend following model for the Australian stock market: $SXE.
The chart tells you what trend following really asks from you. The stock did nothing for almost two years, sitting around 0.70 AUD. Then a first leg up — and then 𝘁𝘄𝗲𝗹𝘃𝗲 𝗺𝗼𝗻𝘁𝗵𝘀 𝗼𝗳 𝘀𝗶𝗱𝗲𝘄𝗮𝘆𝘀 𝗰𝗵𝗼𝗽 between roughly 1.40 and 2.00, which is exactly the stretch where most people throw a trend position away.
My model entered at 1.887 AUD. Today: 4.84. That is +𝟭𝟱𝟲% 𝗼𝗻 𝘁𝗵𝗲 𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻, roughly 6x from where the stock was doing nothing in 2022, and +7.44% this week alone.
I did not predict any of this. The model held through the boring part because the rules said hold. For trend following it is important to cover several markets. In an ideal case Australia with small caps and large caps, Canada, Germany — and the US as an addition, although the risk-reward on the US market is not that interesting to me. Quite different for mean reversion and relative momentum strategies.
Michael retweeted
Wouter Keller wanted to keep crash protection simple and spend less time sitting on the sidelines. He was also dissatisfied with the complexity, high fees and one-size-fits-all products offered by big banks. He wanted a more transparent, rules-based approach that investors could understand.
He and Jan Willem Keuning built HAA-Balanced around that goal.
Each month, it selects the strongest four of eight ETFs across stocks, real estate, commodities and Treasuries. Weak holdings give way to defensive assets. When inflation-protected bonds flash a warning, the whole portfolio retreats to T-bills or intermediate Treasuries.
This ETF model is inspired by HAA and fits closely to its set of rules. Public link in comments. $SPY $IWM $IEF
Michael retweeted
How to build a Claude trading bot for free:
Step 1: Get the tools
Download Claude desktop at claude.ai/download, open Claude Code.
Use tradingView account at tradingview.com, the free plan gives 2 alerts which runs 2 bots.
Use an exchange that connects to the automation:
I use bybit.com.
Step 2: Give Claude a backtesting engine
Claude can't backtest TradingView strategies on its own
So set up a free account at tradingkit.com.
Go to "My Account" and copy the line of code.
Paste it into Claude Code, say "install this globally", paste your authorisation code when it asks.
Step 3: Check it works
Use this prompt:
"Backtest a simple strategy on BTCUSDT 1 hour, just to confirm we're connected"
Step 4: Build the bot
Use this prompt:
"Research trading concepts, build strategies and backtest each on BTCUSDT 1 hour with fees and slippage on. Reject anything under 100 trades, over 20% drawdown, profit factor under 1.1, or that fails to beat buy and hold. No multiple timeframes, no trailing stops, every exit fixed at entry. Log everything and show me the survivors"
Step 5: Verify it yourself
On TradingView open the Pine Editor, paste the winning code, save, add to chart.
Profit factor, drawdown and trade count should match what Claude reported.
Step 6: Forward test it
Leave it running on live data for 20 trades minimum.
99% of backtests fail here.
Step 7: Get your exchange keys
On your exchange create an API key and secret, trading access only, never withdrawals.
A bot can only lose what it can reach.
Step 8: Connect it
Connect it via trigger.trade which sits between TradingView and your exchange.
It's free and the keys are encrypted on your machine.
Pick your exchange, then paste the keys.
Then on TradingView: new alert, paste trigger.trade's message, paste the webhook URL, set fills only and create.
Step 9: Give it a risk manager
Paste into Claude Code: "Keep an eye on my strategies. If any hits 4% drawdown, pause it. If you think one is losing its edge, pause that too. Never restart anything, only message me"
Every trade now fires entry, stop loss and take profit in one order.
The bot trades and the agent protects it.
All you need to do is check a report.
Michael retweeted
I publish a Risk On/Risk Off indicator for my subscribers. It's based on an oscillator using NYSE Advance/Decline issues. Yesterday it flipped to Risk Off. Here are the triggers for the last 13 years. It was Risk Off about 25% of the time. Just one more check-mark on the scoreboard.
Michael retweeted
Same logic. Same settings. Different universe.
Two days ago I shared the updated OOS results for my Nasdaq long mean-reversion model.
The question I got: does the same logic work on S&P 500 stocks?
So I changed only the universe. Using a survivorship bias free database with historical S&P 500 constituents.
Results:
OOS since 1/1/2024
• CAGR: 42.12%
• Max drawdown: -10%
• Sharpe: 1.79
Full backtest since 1999
• CAGR: 25.32%
• Max drawdown: -21.83%
• Sharpe: 1.28
Average capital exposure: 25.75%.
Compared with the Nasdaq version, exposure is higher, but so is CAGR, while the OOS drawdown remains around the same level.
I published the complete rules + interactive backtester for free here:
crackingmarkets.com/buying-s…
Free registration is required so the material is not openly indexed and scraped by AI bots.
Michael retweeted
Beating Nasdaq 100 buy-and-hold with only 15% average capital exposure.
And the rules are public.
My long only Nasdaq mean reversion strategy has added some nice gains in September.
Over the last few years, out of sample:
• CAGR: 35%
• Max drawdown: −11%
• Sharpe: 1.79
You do not have to take my word for it.
I share the rules, code, and even a free backtester on my blog. You can inspect the logic and test it yourself.
The Resource Room → Interactive Models → Buying Short-Term Dips in Stocks
Michael retweeted
How do I know whether a trading system is broken, or just going through an unfavorable market?
One check I find useful: compare its equity curve with those of similar models.
For me, this starts with trading simple models whose logic I understand. Not a pile of data mined rules whose only justification is a nice backtest.
Over time, I usually find other people trading the same basic idea. Their equity curves become useful reference.
The chart shows the live trading performance of Ares, tracked on Collective2.
I do not know the developer, but the system appears to trade NQ intraday breakouts, similar to what I trade. It trades more frequently, but the overall behavior looks very familiar.
Neither equity curve has been making new highs lately.
That does not mean a system is broken. Sometimes the market favors your approach. Sometimes you have to be patient.
What would concern me is a comparable model consistently making new highs while mine kept struggling.
That would make me question whether my rules were too closely fitted to the past, rather than simply blaming the market.
I use the same exercise for my other trading styles too.
Michael retweeted
We opted not to track Carlson's Adaptive 60/40, but it is surprisingly tax efficient; even more so than B&H 60/40 thanks to reduced reliance on tax inefficient UST funds.
~88% of historical gains would have come from LT CG and qual dividends vs 84% for B&H.
New blog post on Tom Carlson's "Adaptive 60/40 Portfolio" -> portfoliooptimizer.io/blog/t…
In this blog post, I analyze that trading strategy (rationale, performances) and I propose a variation based on the Probabilistic Sharpe Ratio to weight momentum signals.
Michael retweeted
After 40 years in this business, the #1 pet peeve for investors is losing money, or more specifically, suffering drawdowns.
And due to sequencing risk, drawdowns become even more important as we age.
Which is why several years ago I embarked on building a portfolio that weathers turbulence and keeps drawdowns and volatility to an absolute minimum without detracting too much from returns.
In simulations, the All-Weather Portfolio generates a CAGR of 15.3% with a maximum drawdown of -9.03%.
In real-time trading since release, the same portfolio is tracking at a CAGR of 18.9% with a maximum drawdown of -9.01%
The portfolio is available via our membership and managed accounts (for Australian wholesale investors only) and, soon, a retail managed fund.
Michael retweeted
A very interesting project to check out for anyone exploring systematic trend following and momentum in futures.
It builds heavily on Robert Carver’s work, but with some different implementation choices.
What I particularly like is the dashboard. It makes a complex portfolio easier to follow, showing orders, daily P&L drawdowns etc.
There is also an ongoing paper trading run, with daily updates published to GitHub. So you can study the implementation and follow how the portfolio behaves as new data arrives.
Worth exploring for ideas, even if your own approach is different.
github.com/Lucas-Joly-GH/tre…
Michael retweeted
Mean reversion systems are usually associated with fast entries and short holding periods.
This one is deliberately slow.
It is an RSI based model built on a weekly timeframe and applied to S&P 500 constituents, with many years of out-of-sample history.
I would not allocate an entire account to it. Its CAGR is not especially attractive relative to the average capital it employs.
But Inside a broader portfolio, it can provide useful diversification through slower signals, longer holding periods, and different behavior from faster mean reversion systems.
I publish its current open positions on my blog under:
Live Trading Models → Buy the Dip (Weekly)
A system does not need to be exceptional on its own to improve a portfolio.
Michael retweeted
Even a simple trend following system on individual stocks can better than passive buy and hold, in my view.
The one I trade is straightforward:
• Select a stock universe for the month. For example using momentum.
• Trade long Donchian breakouts within that universe throughout the month.
• Manage exits with an ATR trailing stop.
• Include gold through GLD to diversify beyond stocks.
Not a holy grail. But a useful addition to my other stock systems. It also leaves capital available that I can put to work elsewhere in the portfolio.
The ATR trailing stop gets me out relatively quickly when a move reverses.
The biggest potential improvement I see is in universe selection.
Breakouts with trailing stops work well on stocks making strong, sustained moves.
Currently, I use a very simple momentum based approach to select the monthly basket.
But I am interested in testing whether AI could help identify stocks that are “hot” for fundamental reasons.
