Risk Architecture & Market Regimes
Following the Trend: Diversified Managed Futures Trading
Clenow opens up the trend-following desk: one plain rule set, run on dozens of unrelated futures markets, with every position sized from volatility so each market risks the same small slice of equity. The rules are simple; the book's point is that diversification, sizing and patience do most of the work.
The big picture
Andreas Clenow, a Zurich-based hedge fund manager who earlier ran equity and commodity quant modelling at Reuters, wrote Following the Trend (Wiley, 2013; a second edition followed in 2023) to show how the managed-futures industry actually makes its money. Managed futures funds, often called CTAs (commodity trading advisors), trade futures on stock indices, government bonds, currencies, energy, metals and farm goods, long and short. The book's argument is that most of what the large trend-following funds deliver can be traced back to fairly simple rules. A reader who understands a moving-average filter, a breakout entry, a stop measured in volatility and volatility-based position sizes already holds most of the engine.
What separates a professional desk from a hobby system, in the book's telling, is not a secret indicator but the surroundings: a broad and balanced list of markets, a risk budget per position that stays the same in calm and wild markets, and the patience to hold a winner for months while most trades lose small. The book then goes through the strategy's results year by year, including the flat and losing years, so the reader sees how uneven the path is. The second edition extends that review and describes how the industry and the strategy fared in the decade after the first. Why it matters now: after a long stretch in which one stock market theme carried most portfolios, a rulebook that trades bonds, currencies and commodities with equal risk is the clearest way to see what real diversification looks like in numbers.
The 3 strategic pillars
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A simple core, written down
The book strips trend following down to a few mechanical parts that anyone can code and check: a filter that only allows trades in the direction of the longer trend, an entry when the price breaks out of its recent range, and an exit that trails behind the price. Complexity is not where the returns come from.
A typical version: the trend counts as up when a faster moving average of closes sits above a slower one (for example 50 and 100 days), and down when it sits below. With the trend up, a close above the highest close of the prior 50 days opens a long; with the trend down, a close below the prior 50-day low opens a short. The only exit is a trailing stop a fixed number of ATR units behind the best close since entry. ATR, the average true range, is the average daily price swing, so a stop of 3 ATR sits three typical days away.
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Size by volatility, not by conviction
Every position gets the same risk budget. A quiet bond future and a jumpy crude oil future both move the account by roughly the same amount on a normal day. Without this step, the most volatile market would dominate the portfolio and the list of markets would be diversified in name only.
Contracts = equity × risk factor ÷ (ATR × point value), where the point value is the money gained or lost per contract for a one-point price move. With a risk factor of 0.20% of equity, a one-ATR day moves each position by about 0.20% of the account, and a stop three ATR away costs about 0.60% if it is hit straight from entry. Because size is tied to current equity, positions shrink after losses and grow after gains, which keeps the risk per trade stable through a drawdown.
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Diversification and a lumpy path
Most trades lose a little; a few long trends in a few markets pay for everything. Nobody knows in advance which market will produce the year's big trend, so the system has to be in all of them. The book shows years in which the whole industry made large gains and years in which it gave back part of them.
Run the same rules on many unrelated markets and add up the daily profit and loss. The equity curve rises in steps during broad trends (a bond rally, a currency slide, a commodity boom) and drifts sideways or down in range-bound years, when breakouts fail and stops get hit, a pattern known as whipsaw. The quantities to watch are the maximum drawdown, its length, and how much of the total profit came from the best few trades.
What a Closelooknet reader does with it
The working use is to test a trend rulebook the way the book describes it, on several markets at once and with volatility-based sizing, instead of judging one chart at a time. A reader fills in daily closes and point values, sets the risk per position and the stop distance, and sees the combined equity curve, the drawdowns and which markets carried the result. The first mistake it prevents is sizing by contract count or gut feeling, which lets one volatile market swamp the rest. The second is judging a trend system on a single market or a single good year: the book's own year-by-year record shows long flat and losing stretches, and the worksheet makes them visible before anyone commits to the idea. The parameter defaults in the pack are Closelooknet's starting values, not settings taken from the book; its output is a backtest of past data, not a signal.
The bridge to the Closelooknet approach
The shelf already holds the general case: the Trend Following method dossier covers the academic evidence for time-series momentum, the twelve-month trend signal and why trend tends to pay in long sell-offs. Clenow's book is the desk version of the same idea, spelled out rule by rule for a futures portfolio: which filter, which entry, how far the stop, how many contracts. For sizing beyond a fixed risk factor, Fortune's Formula explains the Kelly logic of betting a fraction of capital that depends on the edge, and The Black Swan is the reminder that the rare large move is where trend systems earn most of their money, in both directions. Adaptive Markets offers one explanation for why trends keep appearing and why the strategy goes through long dry spells. On Closelooknet, the Market Regime framework reads whether markets are trending or range-bound, which is the condition that decides whether a breakout system earns or bleeds, and the Factor Regime lab compares a momentum tilt with a low-volatility tilt on equities. The glossary entries for moving average, ATR, trend following and realized volatility define the pack's inputs.
Action-Kit — from theory to practice
Tooling & data
| What you need | Where to get it | Cost |
|---|---|---|
| Futures prices and contract specifications Daily closes, contract sizes and point values for the markets in a diversified list | Barchart Delayed quotes and contract specifications are free; long back-adjusted history usually needs a paid data plan. | Freemium |
| Exchange contract specifications Official contract size, tick size and trading hours for each future, to set the point value correctly | CME Group Eurex and ICE publish the same for their contracts. | Free |
| Positioning of large traders Weekly Commitments of Traders data to see how speculators, including trend followers, are positioned in each market | CFTC Context only; it does not enter the pack's rules. | Free |
| Backtesting in Python Open-source environment for running the same rules on larger futures datasets with rolls and costs | Python (python.org) The pack's script needs only the standard library; larger studies usually add a dataframe library. | Free |
The formulas
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Trend filter
Trend = +1 if MA_fast > MA_slow; −1 if MA_fast < MA_slow; else 0- MA_fast = simple average of the last F closes (pack default 50)
- MA_slow = simple average of the last S closes (pack default 100)
The filter only allows entries in the direction of the trend; it does not force an exit.
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Breakout entry
Long if flat, Trend = +1 and Close > max(Close over prior N days); short if flat, Trend = −1 and Close < min(Close over prior N days)- N = breakout window (pack default 50 days)
- Close = daily close of a back-adjusted continuous series
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ATR position size
Contracts = ROUNDDOWN(Equity × r / (ATR × PV))- Equity = account value the day before entry
- r = risk factor per position (pack default 0.20%)
- ATR = average daily range; PV = money per one-point move per contract
Example: equity 10,000,000, r 0.20%, ATR 40, PV 50 → 10 contracts; a 1-ATR day ≈ 20,000 (0.20%), a 3-ATR stop ≈ 60,000 (0.60%).
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Trailing stop in ATR
Stop_long = highest close since entry − k × ATR; Stop_short = lowest close since entry + k × ATR- k = stop distance in ATR (pack default 3)
- exit when the close crosses the previous day's stop
Drawdown = equity ÷ running peak − 1. The pack's ATR is a close-to-close stand-in for the high-low true range.
Applied Pack · free members
Clenow Applied Pack
A diversified trend-following backtest you can trace cell by cell: moving-average filter, breakout entry, ATR stop and volatility-based sizing on several markets at once, with the combined equity curve and drawdown. Your closes in, a backtest out; software for your own research, never signals.
- Clenow_Trend_Backtest.xlsx — READ ME; Settings (amber equity, risk in basis points, moving-average, breakout and ATR windows, stop distance, cost, plus a single-trade sizing calculator); Summary (ending equity, total and annualised return, maximum drawdown, trades and P&L per market, equity and drawdown charts); Portfolio (common dates, daily equity, peak, drawdown); four EXAMPLE_ market sheets with trend, breakout, ATR, position, stop, contracts and daily P&L as live formulas
- clenow_trend.py — stdlib-only backtester: reads a CSV of your own daily closes for any number of markets and prints trades, P&L per market, ending equity, annualised return, maximum drawdown and a yearly table; a size subcommand checks a single position; same rules and same results as the workbook
- prices_sample.csv — synthetic daily closes for four EXAMPLE_ markets with point values, showing the input format
- README.txt — which parts are the book's and which are the pack's defaults and stand-ins (close-to-close ATR, no contract rolls), every rule and formula, how to run, limits and the educational-use disclaimer
Pack security
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Educational templates — a research diary companion, not investment advice.
Closelooknet publishes a market diary, not investment advice. This condensed read restates the book's ideas in our own words for education — for the author's full argument, go to the source.