Quantified Strategies

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Rule-Based Trading Community - Skool

We’re no longer very active here.

Most of our quantified strategies, backtests, and trading discussions are now shared inside our Rule-Based Trading community on Skool.

Join us there if you want to learn systematic rule-based trading.

Rule-Based Trading

[quantifiedstrategies]

Meb Faber’s Momentum and Trend-Following Explained

Today, we look at Meb Faber’s Momentum and Trend-Following Strategy For Gold, Stocks, and Bonds, which he published in a paper in 2015.

Most investors are attracted to complexity.

Here’s an example of how you can utilize the momentum factor in practice:

A momentum investor buys stocks that have performed best over the last 6 months (can be 3, 9, or 12 months)

The investor rebalances his portfolio every month or every 3 months by selling the least performing ones and buying new stocks that are currently performing best.

Pretty simple. Rinse and repeat 4 times per year.

Meb Faber suggested using a variation of the above for the most obvious three asset classes. You find the trading rules at the end of the article.

Meb Faber’s Momentum and Trend-Following Strategy - Backtest

We backtested it from 2010 until today (it has performed well before that):

Performance

No. of trades: 382

Average gain per trade: 0.7%

Win ratio: 55%

Profit factor: 1.6

Annual returns (CAGR): 8.7%

Exposure/time in the market: 92%

Risk-adjusted return: 9.3% (CAGR divided by time spent in the market (0.92))

Max drawdown: -15%

And Meb Faber’s Three-Way momentum/trend-following strategy is really simple (it’s not a timing model). Here is all there is to it:

Three asset classes: Stocks, bonds, gold. Invest equally in whatever is going up.

His paper didn’t mention capital allocation, but we assume the entire equity is divided equally, or 100% if only one asset class has positive momentum in any month. If there are two positions, 50% each, etc.

[quantifiedstrategies]

VIX Trading Strategy

VIX trading strategy shows how sharp spikes in market fear can be used as a structured edge in the S&P 500 instead of something to react to emotionally.

When volatility surges, it usually reflects a sudden shift in sentiment rather than a lasting change in direction. In these moments, options pricing expands, the VIX jumps, and uncertainty peaks. What matters most is that these conditions rarely persist for long. Markets tend to stabilize once the initial wave of panic is absorbed.

The core idea behind this approach is simple: fear in markets is often temporary and tends to unwind faster than most participants expect. When volatility becomes extreme, it often signals that positioning has become one-sided and that a short-term reversal phase may follow.

The historical results highlight the behavior clearly:

324 trades

Average gain: 0.6%

CAGR: 5.2%

Time invested: 14%

Max drawdown: 23%

Risk-adjusted return: 36%

What makes this interesting is not just the return, but the structure behind it. The strategy stays exposed only part of the time while focusing on specific moments when sentiment reaches extremes. That makes the profile more about selectivity than constant market exposure.

At its core, this is a volatility-driven mean reversion approach. It does not try to predict long-term direction. Instead, it focuses on the repetitive cycle of panic and recovery that shows up in equity markets over time.

The key takeaway is straightforward. Volatility spikes are not just noise. They are often temporary dislocations where short-term opportunity becomes more concentrated than usual.

[quantifiedstrategies]

Fabian Timing Model Beats The Market

The Fabian Timing Model is a simple, quantitative long-term trend-following strategy for the stock market. It uses an intermarket signal derived from the S&P 500, the Dow Jones Industrial Average, and the utilities sector.

The model was created by Richard Fabian, an author, speaker, and money manager. He describes the strategy in his book The Mutual Fund Wealth Builder.

Although he argued that no one could come up with consistently good market timing predictions on their own, nor was he smart enough to assess all available information correctly, he strongly recommended adhering to some mechanical system. Also, he did not say that his model was perfect, but that by trying to perfect any timing model, we would have no strategy.

We backtested it using Python (trading rules beneath the equity curve):Fabian Timing Model

CAGR is 6.95% (buy and hold 4.90%)

The standard deviation is 9.87 (17.96)

Time spent in the market is 56.20%

Risk-adjusted return is 12.34% (CAGR divided by time spent in the market)

Total trades were 31

Maximum weekly drawdown is 16.38% (56.52%)

Fabian Market Timing Model trading rules:

A full breakdown of this backtest and its trading rules is published daily in our Skool community.

[quantifiedstrategies]

A Very Simple Trading Rule That Can Make You Money

Most traders think profitable trading needs complexity. Paul Tudor Jones built one of his most famous trades around a very simple rule: the 200-day moving average.

If price is above the 200-day moving average, stay in the market. If price is below it, get out.

That single filter helped traders avoid major crashes like the dot-com collapse and the 2008 financial crisis while keeping long-term returns similar to buy and hold with smaller drawdowns.

Even more interesting, the MLM Index uses this same concept across 25 futures markets including commodities, currencies, metals, and bonds. The strategy has delivered stock-like returns with lower risk and low correlation to stocks.

Sometimes the best trading rules are the simplest ones.

[quantifiedstrategies]

Commodities vs. Stocks: Which should you own right now?

Timing the market is hard, but what if you could simplify your decision by watching just one ratio?

In our latest analysis, we dive into the Commodities-to-Equity Ratio—a powerful relative-strength indicator that compares the S&P GSCI to the S&P 500. Instead of guessing where inflation or interest rates are headed, this ratio lets the market tell you where the momentum is.

Why does this matter for your portfolio?

Regime Changes: The ratio has gone through massive cycles, favoring stocks in the 90s and commodities in the 2000s.

Diversification: Commodities often move independently of stocks, offering a potential hedge during geopolitical tension or supply shocks.

The Strategy: We backtested a simple trend-following rotation strategy. The result? An 8.96% CAGR, outperforming the S&P 500's buy-and-hold return.

When the ratio shifts, the entire market regime might be changing. Are you prepared for the next rotation?

[quantifiedstrategies]

Jim Simons: Science is the Key to Success

[quantifiedstrategies]

The Ultimo Effect In Stocks

Ultimo Effect in stocks is one of those market tendencies that quietly keeps showing up year after year.

Most traders focus on news and fundamentals, but some of the strongest short-term edges come from simple money flows. At the end of the month, pension contributions, fund rebalancing, and institutional buying can create a natural upward drift in the market.

We backtested a simple Turn of the Month setup: • 794 trades • 61% win rate • 0.6% average gain per trade • Only 33% market exposure

One of the biggest lessons from this effect is that context matters. A small seasonal edge can become much more powerful when combined with trend or mean reversion strategies.

Sometimes the best trading ideas are hiding in plain sight.

[quantifiedstrategies]

Calendar Trade for Bonds: Simple Construction, Powerful Results

In this post, we’re breaking down a "Calendar Trade for Bonds" that is surprisingly simple to build yet has historically delivered powerful results. This isn't just about random price action; we believe the strategy’s consistent performance is rooted in a genuine structural inefficiency within the bond market.

Calendar Trade for Bonds

The strategy returned the following equity curve (TLT):

Performance

No. of trades: 284

Average gain per trade: 0.35%

Win ratio: 61%

Profit factor: 1.8

Annual returns (CAGR): 4.5%

Exposure/time in the market: 14%

Risk-adjusted return: 32% (CAGR divided by time spent in the market (0.14))

Max drawdown: -15%

Trading Rules

The strategy is based on the following trading rules:

Buy at the close a few days before the end of the month (max 7 days before the end). Sell at the close on the last trading days of the month.

[quantifiedstrategies]

Multiple Days Up And Multiple Days Down Strategy (Backtest)

Today, we’re taking a closer look at Larry Connors’ Multiple Days Up And Multiple Days Down trading strategy.

If you’ve explored short-term trading systems before, chances are you’ve heard of Larry Connors. He is widely regarded as one of the pioneers of mean reversion trading, particularly for his work on identifying and trading short-term market extremes.

One of his lesser-discussed, but surprisingly powerful, ideas revolves around a simple observation: markets tend to overreact in clusters. When prices move up several days in a row, or fall persistently over multiple sessions, they often set the stage for a reversal rather than a continuation.

This behavior forms the foundation of what’s often referred to as the Multiple Days Up and Multiple Days Down strategy.

At its core, the concept is almost deceptively simple. Instead of chasing momentum, the strategy looks for stretches of consecutive gains or losses, periods where traders may have pushed prices too far in one direction. These streaks can signal short-term exhaustion, creating opportunities to trade against the recent move.

But like most Connors-style approaches, the edge isn’t just in spotting these pattern, it’s in how they’re filtered, timed, and executed. The real nuance lies in defining what qualifies as “multiple days,” how to confirm the setup, and how to manage risk once you’re in the trade.

In this article, we’ll explore the intuition behind the strategy and why it works. For those interested in applying it in practice, the exact rules and implementation details are where the real edge lives.

Multiple Days Up And Multiple Days Down Trading Strategy

We backtested the strategy on SPY, the ETF that trackss the S&P 500 (trading rules are at the bottom for paid members):

Performance

No. of trades: 267

Average gain per trade: 0.5%

Win ratio: 75%

Profit factor: 2.5

Annual returns (CAGR): 3.9%

Exposure/time in the market: 10%

Risk-adjusted return: 39% (CAGR divided by time spent in the market (0.1))

Max drawdown: -13%

Trading Rules

All the trading rules are published inside our Skool community. At the moment it's free for the first 100 members. We are at 67 right now. We launched last week. https://www.skool.com/rulebasedtrading

[quantifiedstrategies]

Short Strategy for Chinese Stocks (FXI)

I have a quick strategy for Chinese stocks using FXI. This ETF is a great choice because it is incredibly easy to trade with high volume.

For years, I focused almost exclusively on buying stocks and ETFs. It is a natural approach because the market tends to rise over time and economies generally expand. For many traders, buying the dips simply feels right.

Certain markets behave very differently overnight than they do during the trading day. In particular, China-related equities, represented by FXI, often show a persistent pattern between the U.S. close and the next open.

In this article, I’ll walk you through a strategy that systematically shorts FXI from the close to the next open.

Why Shorting Is Difficult

First, some words about shorting:

Shorting is one of those things that sounds easy in theory but is much harder in practice.

First, there’s the asymmetry. When you buy a stock, your downside is capped at -100%, but your upside is theoretically unlimited. When you short, it’s the opposite: your maximum gain is limited, while your losses can, in principle, keep growing. That alone changes how you think about risk.

Then there are the practical frictions. Borrowing shares isn’t always straightforward, and costs can eat into returns. On top of that, markets have a structural upward drift over time, which means you’re often fighting the long-term trend.

And maybe most importantly, shorting is psychologically demanding. Markets can move sharply against you, squeezes happen fast, and being “right eventually” doesn’t help if you’re forced out along the way.

Why Shorting Still Matters (Diversification)

Despite all these challenges, shorting plays a crucial role in a well-rounded trading approach.

The biggest advantage is diversification. Most strategies, especially in equities, are implicitly long-biased. They tend to perform well in bull markets and struggle during drawdowns.

Short strategies, when designed properly, can behave very differently. They can provide returns in environments where long strategies stall or lose money.

These types of edges can be largely uncorrelated to traditional long exposure, making them valuable as part of a broader portfolio.

In other words, shorting isn’t just about profiting from falling prices, it’s about adding a different return stream, one that can smooth your equity curve and make your overall strategy more robust.

Short Strategy for Chinese Stocks (FXI)

This is the equity curve of a “crude” trading idea:Short Strategy for Chinese Stocks (FXI)

Performance

No. of trades: 765

Average gain per trade: 0.1%

Win ratio: 50%

Profit factor: 1.2

Annual returns (CAGR): 2.7%

Exposure/time in the market: 14%

Risk-adjusted return: 19% (CAGR divided by time spent in the market (0.14))

Max drawdown: -17%

The strategy, in itself, is not tradeable under the applied trading rules. But it can easily be improved.

PS. We are launching a new skool community. Free backtested trading strategies every day. https://www.skool.com/rulebasedtrading/about

[quantifiedstrategies]

Seasonal Strategy for Bonds (Including Rule-Based Backtest)

Today, we look at a seasonal strategy for bonds, a part of the market many retail traders tend to overlook. That’s a pity, because bonds can be useful for strategy diversification.

I’ve always liked the quieter corners of the market. The places where the pace is slower, the stories are less crowded, and any edge, if there is one, tends to be small and subtle rather than dramatic.

Bonds fit that description for me, even though the bond market is larger than the stock market. They don’t attract the same attention, and maybe that is part of the appeal.

Over the years, I’ve noticed that certain seasonal patterns seem to appear again and again in bond markets. Not perfectly, and certainly not in a mechanical way, but often enough to make them worth studying.

This is not about predicting interest rates or making bold macro calls. It is more about observing whether flows, positioning, and calendar effects can tilt the odds slightly at certain times of the year, month, or even day of the week.

That is the idea behind this seasonal swing trade. Nothing fancy. Just a simple framework I’ve found useful to revisit from time to time.

I would not call it a complete strategy on its own. It is more of a lens. Sometimes it lines up with other signals I watch, and sometimes it does not. But when it does, it can add a little more structure to trades that might otherwise feel like guesses.

Seasonal Strategy for Bonds - Backtest

Let’s run a backtest of the strategy. The strategy trades both long and short in

$TLT

Seasonal Strategy for Bonds

No. of trades: 569

Average gain per trade: 0.43%

Win ratio: 60%

Profit factor: 1.6

Annual returns (CAGR): 10.1%

Exposure/time in the market: 62%

Risk-adjusted return: 16% (CAGR divided by time spent in the market (0.62))

Max drawdown: -24.

I believe these are pretty solid numbers for the seasonal swing trade in bonds, especially considering it also involves trading from the short side.

Trading Rules

Want the breakdown of the seasonal strategy for bonds?

[quantifiedstrategies]

I started with pairs trading

I started pairs trading when I got into full-time trading in 2001. I was a 100% market-neutral trader.

Why did I start with pairs trading?

It was one simple reason: my mentor Steinar, a fellow Norwegian who had lived and traded in Chicago, traded pairs and merger arbitrage.

Steinar was a successful trader. (The best you can do as a trader is to get a foot inside an office or get in touch with people who are profitable.)

My pairs trading strategies gave me a good starting point.

At the time, pairs trading was highly likely much more profitable than it is today.

I traded only stocks listed on NYSE – no NASDAQ pairs.

At the time, the specialist on the NYSE floor had a lot of power, and most of the trading went through those firms.

The specialist’s job was to create a fair and orderly market and to fill big positions.

The specialist system often meant I got price improvements:

If I had a bid at 50.25 and the specialist received a sell order for 40 000 shares, he might “sweep” all the bids and fill the order at 50.05.

Thus, I managed to buy at 50.05 instead of 50.25.

If I were filled, I could either be long or short the other stock to make a “market-neutral” position.

It’s unlikely that you'll get such a price improvement anymore.

At the time, you could only short on rising prices, so getting a short might be difficult.

Hence, we bought “bullets” (using options so we could sell – not short).

I can recall some of the most popular pairs at the time:

RDC / ESV SII / BJS BHI / SLB FRE / FNM ETG / EVT RQI / RPF BSC/LEH MS/GS STT/STI

Traders starting out today will notice that many of the tickers no longer exist.

All pairs were traded intraday, ie. as day trades.

I stopped pairs trading in 2005 because I found better strategies, and most likely I didn’t manage to adapt to the dynamics of pairs trading.

However, even today, after pairs trading has been around for over three decades and the most obvious edges are “arbed” away, there are still profitable pair traders out there. I still believe Bright Trading has a pairs trading group.

- Oddmund Groette

[quantifiedstrategies]

Martingale Backtested: Why It Fails in Modern Markets

Ever heard of a strategy that promises a near-100% win rate? It sounds like the Holy Grail, but in the world of trading, it’s often a wolf in sheep’s clothing. Let’s talk about the Martingale Strategy.

What is it?

Originating from 18th-century French gambling, the Martingale is a position-sizing method with a simple (and dangerous) rule: double your bet after every loss. The theory is that eventually, you’ll win, and that one win will recover all previous losses plus a small profit.

How it looks in the Markets:

Traders often apply this through:

Averaging down: Buying more shares as the price drops.

Grid Trading: Systematic buy orders at set intervals.

The "Anchor" Effect: You become psychologically anchored to a price, doubling down to lower your break-even point.

The Mathematical Illusion 📉

On paper, Martingale looks foolproof because:

Losses grow geometrically ($1, 2, 4, 8, 16\dots$).

A single win wipes the slate clean.

It assumes you can’t lose forever.

The Reality: Exponential Risk

In the real world, Martingale is a "ticking bomb." Why?

Capital Exhaustion: After just 10 losses, your position is 1,000x larger than your start. You will likely run out of money before the market turns.

Markets aren't Coin Tosses: Unlike roulette, markets can trend in one direction for months or years. They don't have to revert to the mean just because you're losing.

The "Smooth" Trap: Backtests often show a beautiful, steady equity curve... right until it drops off a cliff to zero.

The Verdict: Martingale transforms frequent small gains into rare, catastrophic losses. It’s not about if it fails, but when.

Full breakdown here.

[quantifiedstrategies]

How Ken Griffin Trades: The Strategies Behind Citadel’s Success

I have dedicated years to studying the elite figures who consistently outperform the market through endless books and articles. Ever since reading Jack Schwager’s Market Wizards in the mid-90s, I have kept his entire collection on my bookshelf as a constant reference.

Most legendary investors are defined by a specific signature style. Warren Buffett is the face of value investing, George Soros is known for reflexivity, and Jim Simons mastered the world of pure mathematics and quant.

Ken Griffin?

He doesn’t have one of these “things.” Instead, he built a machine.

The myth of the lone genius trader

When I first read about Ken Griffin, I expected another story about intuition and bold bets and data.

Instead, I found something completely different.

Griffin started like many of us: trading from his dorm room, even installing a satellite dish just to get better data.

But somewhere along the way, he abandoned the idea that one person could consistently beat the market.

Instead, he built Citadel into a multi-strategy system: a collection of teams, models, and ideas all competing internally.

That’s the first big lesson:

Griffin didn’t try to become a better trader. He built an environment where many traders could win at once.

Trading as a system, not a strategy

Most traders are obsessed with finding the strategy.

Griffin did the opposite.

Citadel (his hedge fund) trades:

Equities

Fixed income

Commodities

Currencies

Derivatives

It’s both diversification and parallel experimentation.

Different teams run different approaches:

Quant models

Fundamental long/short

Macro trades

Short-term statistical arbitrage

Some strategies hold positions for minutes. Others for months. It’s a portfolio of edges.

You should think similarly about your own trading. Trade different markets/assets, different time frames, both market directions, and different types of strategies.

The obsession with market neutrality

One thing that really stood out to me is Griffin’s focus on not being dependent on the market direction.

Citadel often runs market-neutral strategies, balancing long and short positions.

Data beats opinion

If there’s one theme that runs through everything Griffin does, it’s this:

Data > narrative.

Citadel was early in:

Quantitative models

Alternative data

Machine learning

And today, they process massive datasets to find tiny edges.

However, as far as I know, it’s not just quant. Griffin blends:

Human judgment

Fundamental research

Systematic signals

That hybrid approach is probably the real edge. However, that is, of course, hard to replicate.

The uncomfortable takeaway

Of course, replicating Ken Griffin's strategy is basically impossible for small, independent traders.

You and I don’t have:

Thousands of researchers (Citadel has thousands)

Petabytes of data

Dozens of competing teams

But we can still learn from him. Here’s what I’m taking away:

Think in systems, not trades

Diversify edges, not just assets

Focus on risk before returns (preserve your capital)

Use data

Avoid dependence on market direction

[quantifiedstrategies]

RSI 30/50 Pullback Strategy for Beginners

Back in 1999, when I first dipped my toes into mechanized trading using MetaStock, I fell for the classic RSI trap. Like most beginners, I treated 30 as a "buy" signal and 70 as a "sell" signal, expecting immediate reversals. I quickly learned the hard way that in a strong trend, indicators can stay "overbought" or "oversold" far longer than your account can stay solvent.

That frustration led to my first real breakthrough: the RSI 30–50 zone. Instead of fighting the momentum, I began using the RSI to identify high-probability pullbacks within an established uptrend. This wasn't just a tweak; it was a fundamental shift from guessing tops and bottoms to following the flow of the market. While this strategy didn't set the world on fire, it taught me the invaluable power of backtesting and automation. Here is how that discovery works.

What the RSI 30–50 Zone Really Means

Instead of using RSI as a reversal tool, I used it as a pullback tool. The key insight for me was this: 👉 The 30–50 zone is where pullbacks happen in an uptrend

How I Trade the RSI 30–50 Strategy

My approach is simple and rule-based (as always). I don’t try to overcomplicate it. (And, by the way, the asset you choose to trade is more important than the strategy.)

1. Identify the Trend First

Only use this strategy in bullish markets.

Price above a rising moving average (like the 200-day) → uptrend

Price below a falling moving average → downtrend

Simple, but efficient.

2. Wait for RSI to Pull Back Into 30–50

In a bullish market:

Wait for RSI to drop below 30

This tells the market is pulling back, not reversing.

4. Enter the Trade

Once you get confirmation, enter long.

5. When To Sell

When the RSI reaches 50, sell. I used a 5-day value (lookback period) of the RSI.

Why This Strategy Works

Instead of fighting the trend, you’ll:

Buying dips in an uptrend

The RSI 30–50 helps you time those entries better.

RSI 30 50 Strategy for Beginners Backtest

Let’s backtest the strategy on the S&P 500:

No. of trades: 196

Average gain per trade: 0.9%

Win ratio: 81%

Profit factor: 3

Annual returns (CAGR): 5.3%

Exposure/time in the market: 11%

Risk-adjusted return: 44% (CAGR divided by time spent in the market (0.11))

Max drawdown: -15.

[quantifiedstrategies]

A Trend-Following Strategy for the S&P 500

A trend following strategy for the S&P 500 is one of those ideas that sounds almost too simple to work: buy when the market is going up, sell (or step aside) when it’s going down.

Yet, despite its simplicity, it has quietly survived decades of market cycles, crashes, and booms.

At its core, it’s not about predicting the future. Instead, it’s about reacting to what the market is doing and staying aligned with that direction for as long as it persists.

The famous Turtle Experiment by Richard Dennis in the 1980s made trend following famous.

Trend Following Strategy for the S&P 500

What makes trend following especially interesting in the context of the S&P 500 is the market’s long-term upward bias combined with its occasional sharp drawdowns.

A trend follower doesn’t try to call tops or bottoms. Instead, the goal is to capture the “meat” of major moves while avoiding the worst of the declines. This often means accepting that you’ll never buy at the exact low or sell at the exact high, and being perfectly okay with that trade-off.

Trend-Following Strategy Drawbacks

Also, you must expect a low win rate and many whipsaws. This is the drawback, and makes it hard to follow the rules. For example, a low win rate means the probability of many consecutive losers rises significantly. Are you able to pull the trigger after 5 losses in a row?

What Makes Trend-Following Appealing?

Personally, I’ve always found trend following appealing because it removes the need for constant opinions. There’s no need to guess what the Fed will do next or whether valuations are stretched. The strategy forces a kind of discipline: follow the rules, trust the process, and let the market dictate your positioning.

It’s not flashy, but over time, that consistency is exactly what makes it powerful.

Furthermore, trend-following is a fantastic diversification from mean reversion strategies. These two types of strategies behave differently - a lot so.

If you are a serious trader, you should try to implement different strategies, market directions, time frames, and asset classes to trade.

Trend Following Strategy for the S&P 500 - Does It Work?

Let me backtest a trend following strategy for the S&P 500:

Since 1960, it has returned 6% for the cash index. That is below the buy-and-hold of 7.5%, but the strategy was invested only 65% of the time. Because of this, the strategy has a better risk-adjusted return (9%). Max drawdown was only 25%, compared to 55% for buy-and-hold.

Trading Rules

The trend following strategy for the S&P 500 used the following trading rules:

Do you want it?