Trend followers are deliberately late
A trend-following strategy waits for evidence of strength before buying, then cuts or reverses exposure after weakness appears. It will never catch the exact bottom or top. The aim is to capture the middle of a sustained move with rules that can survive false starts, trading costs and markets not used in research.
Researchers often call this family time-series momentum: an asset’s own past return helps set its direction. Moskowitz, Ooi and Pedersen found return persistence across 58 liquid futures and forwards. That is a reason to study the idea, not permission to transplant the same parameters into crypto. Crypto trades around the clock across fragmented venues, with unstable liquidity, outages, funding and short-lived assets.
Every trend rule needs four answers
| Decision | Examples | Question the rule must answer |
|---|---|---|
| Signal | Price breakout, moving-average crossover or signed trailing return. | Exactly which completed observation changes direction? |
| Position size | Fixed notional, volatility targeting or stop-distance risk. | How much can one position contribute to loss? |
| Exit | Opposite signal, trailing stop or shorter reversal channel. | What invalidates the position without hindsight? |
| Portfolio rule | Asset caps, correlation limits and aggregate risk target. | What prevents several similar coins from becoming one concentrated bet? |
“Buy when momentum is strong” cannot be tested or reproduced. “At 00:00 UTC, buy when the last completed daily close exceeds the highest completed close of the previous 100 days” is much closer. Even then, the rule still needs a data source, a lookback convention, a policy for missing candles and an order type.
A concrete breakout rule to pick apart
Consider a simple long-or-flat spot rule. Once per day, it checks the last completed candle. It enters when that close exceeds the prior 100-day high, exits when it falls below the prior 50-day low, and risks no more than 0.5% of account equity at a stop initially placed two 20-day average true ranges below the signal price. It caps any one asset at 20% of account equity.
- The current candle is excluded from the breakout range, preventing a look-ahead error.
- A fixed UTC decision time prevents the backtest from quietly choosing favorable bar boundaries.
- The shorter exit channel lets the system leave before a full opposite entry signal appears.
- The risk cap can reduce size below the 20% allocation cap; the larger size never wins.
- A spot-only version goes to cash instead of assuming that every asset can be borrowed and sold short.
These numbers are for teaching, not a suggested setup. A 100-day breakout and two-ATR stop may suit one asset or period and fail badly in another. Writing the rule precisely is useful because someone else can cost it, reproduce it and show where it breaks.
The signal says when; position sizing says how much
One common approach divides the planned dollar risk by the distance between entry and the initial stop. Suppose the account holds $50,000, the risk budget is 0.5% per trade, and two ATRs equal $2,400.
($50,000 × 0.5%) ÷ $2,400 = 0.104 BTC before venue constraints At a $60,000 signal price, 0.104 BTC is about $6,240 of notional exposure, below the example’s 20% allocation cap. The calculation does not guarantee a $250 maximum loss: the entry can slip, the stop can gap, fees still apply and a venue may only accept a different quantity increment. A live implementation should round down, recalculate risk from the expected fill and reject the order if that exceeds its cap.
What one winning trade looks like after costs
Continue the example with an actual entry fill of $60,120 and an exit fill of $65,850 for 0.104 BTC. Those fill prices already include the difference from the signal prices. Gross price profit is $595.92. If the illustrative fee is 0.10% of notional on each fill, entry and exit fees total about $13.10.
($65,850 − $60,120) × 0.104 − $13.10 = $582.82 illustrative net profit Run the same arithmetic on a loser. If a planned $57,600 stop fills at $57,300, the price loss is $293.28 before fees—already more than the original $250 budget. Tests that show tidy winners but perfect stop fills are flattering the strategy. A perpetual-futures version also needs funding, margin and liquidation rules; the futures trading bot guide covers those controls.
Sideways markets are the recurring bill
Trend rules react after price has moved. During a lasting trend, the delay buys some confirmation. Inside a range, it makes the system buy near one edge and leave after the reversal. Several false breakouts can produce a long run of small losses even when the code follows its rule perfectly.
| Regime | Likely behavior | Control to examine |
|---|---|---|
| Persistent expansion | A few positions may run far enough to offset earlier losses. | Do exits allow winners to continue without unlimited giveback? |
| Sideways range | Breakouts repeatedly fail and turnover rises. | Are costs and a maximum risk budget enforced during the losing sequence? |
| Fast reversal | A profitable position can cross its exit before the next scheduled decision. | How are gaps, stops and stale data handled? |
| Volatility shock | Spread, slippage and measured risk can jump together. | Does sizing react before new orders, and can the portfolio de-risk? |
| Correlated crypto selloff | Several nominally different positions may lose as one trade. | Are aggregate factor and venue exposures capped? |
Volatility scaling makes position sizes easier to compare across assets, but it looks backward. It may shrink exposure only after a shock has arrived. Hard caps on notional, leverage and total portfolio risk still matter when the recent volatility estimate is too calm.
Crypto details that can overturn a clean backtest
- Bar construction: A daily crypto candle depends on its UTC boundary and venue; “the daily close” is not universal.
- Order behavior: Market orders seek immediate execution but do not guarantee one price; limit orders can remain unfilled.
- Maker versus taker: Fees can differ according to whether an order provides or removes liquidity.
- Fragmentation: A signal from a composite index may not be executable at the same level on the selected venue.
- Data continuity: Listings, delistings, token migrations and missing candles can distort the investable universe.
- Twenty-four-hour operations: Reconciliation, outages and risk limits still need coverage when no operator is awake.
- Derivative carry: Funding and basis can turn the same price signal into a different net result from spot.
Coinbase’s trading rules show why a candle price is not a fill: order types behave differently, maker and taker fees can differ, and market orders can slip through several prices. Those are Coinbase examples, not universal rules. A live design needs the documentation and historical data for the venue it will use.
How to test the rule without polishing away the losses
- Freeze the hypothesis. Specify signal, bar timing, universe, sizing, exits and benchmark before reviewing results.
- Use a point-in-time universe. Include assets that later failed or delisted; do not backtest only today’s survivors.
- Separate development and evaluation. Keep an untouched period or use walk-forward tests where each decision uses only prior data.
- Model executable fills. Apply venue minimums, spreads, maker/taker fees, slippage, funding, partial fills and rejected orders.
- Stress the assumptions. Double and triple costs, delay entries and exits, and worsen fills during volatile periods.
- Test neighboring parameters. A broad area of similar results is more credible than one exceptional lookback or stop.
- Slice by regime. Report bull, bear, sideways, high-volatility and low-liquidity periods rather than one aggregate ratio.
- Inspect concentration. Show whether a small number of assets or trades produced most of the result.
- Shadow live orders. Compare intended orders with venue quotes and hypothetical fills before committing capital.
- Start bounded and reconcile. Small live exposure should prove orders, balances, fees, exits and failure handling—not revalidate the backtest.
The backtests-versus-live-results guide explains why a research result and a tradable system are different things. The broader crypto algorithmic trading guide covers the steps shared by other strategy families.
What the long-run research can tell us
Long-run studies make trend following worth investigating, but they do not settle the question for crypto. Moskowitz, Ooi and Pedersen studied traditional liquid futures and forwards. AQR’s longer historical reconstruction covers many markets and economic periods, but it is still a simulation with assumptions, published by an investment manager associated with the approach.
Neither paper tells us that one crypto rule, one lookback or one retail bot works today. Crypto histories are shorter, venue conditions change, costs vary by account and yesterday’s winners can disappear. The literature supports testing the hypothesis; it cannot replace the test.
Papers and market references
- Moskowitz, Ooi and Pedersen — Time Series Momentum
- Hurst, Ooi and Pedersen — A Century of Evidence on Trend-Following Investing
- Kathryn Kaminski / Campbell & Company — Quantifying CTA Risk Management (hosted by CME Group)
- Coinbase Markets — Trading rules
- Coinbase — Advanced trading fees
- Commodity Futures Trading Commission — Risks of virtual-currency trading
The rules, sizes, prices and fees above are worked examples. They leave out taxes and personal circumstances and make no claim that a trend-following strategy is profitable today.