0bps

Strategy vault / Trend following

Andrew Abraham Trend Tracking Strategy

The Trend Tracking strategy was proposed by Andrew Abraham in an article titled "Trading the Trend" published in Technical Analysis of Stocks & Commodities magazine in September 1998. It uses the moving average true…

Pine ScriptTrend followingMean reversionVolatilityATR
Author
ChaoZhang
Complexity
Starter · 38 lines
Parameters
3
Backtest bar
1d · swing
Tested on
BTC/USDT · futures
Jump to codeView in vaultOriginal on FMZ

IMG

Overview

The Trend Tracking strategy was proposed by Andrew Abraham in an article titled "Trading the Trend" published in Technical Analysis of Stocks & Commodities magazine in September 1998. It uses the moving average true range and highest and lowest prices to determine the price trend and trade in the direction of the trend.

Principles

The strategy first calculates the 21-day average true range avgTR. Then it calculates the 21-day highest price highestC and lowest price lowestC. Next, it calculates the upper rail hiLimit, which is the highest price minus 3 times avgTR; and the lower rail loLimit, which is the lowest price plus 3 times avgTR. When the closing price exceeds the upper and lower rails, their values are taken as the reference price ret, respectively. When the closing price is higher than the reference price, go long; when lower, go short.

Advantages

The main advantages of this strategy are:

  1. Using average true range to calculate the channel can dynamically capture market volatility.
  2. Combining highest and lowest prices to determine trend direction avoids being misled by oscillating markets.
  3. The logic is simple and clear, easy to understand and implement.
  4. It can trade according to the trend and avoid frequent opening positions in oscillating markets.

Risks

There are also some risks with this strategy:

  1. More false signals will occur in oscillating markets. This can be reduced by optimizing parameters.
  2. Unable to determine trend reversal points, risk of loss exists. Other indicators can be combined to judge trend reversal.
  3. Improper parameter optimization may lead to overtrading or false signals. The stability of parameters needs to be tested carefully.

Improvement

Some ways to improve this strategy:

  1. Optimize the moving average period to find the best parameter combination.
  2. Add stop loss to control single loss.
  3. Combine volatility indicators to determine market conditions and avoid trading in oscillating markets.
  4. Add trend judgment indicators to identify trend reversal points and reduce probability of losses.

Conclusion

In summary, the Trend Tracking strategy is a simple and practical trend trading strategy. It uses price channels to determine trend direction and avoid being trapped in oscillating markets. But there are still some risks, and further optimizations are needed to improve stability. It is suitable for investors with some trading experience.

Parameters

NameDefaultWhat it controls
v_input_121Length
v_input_23Multiplier
v_input_3falseTrade reverse

Source Pine Script

/*backtest
start: 2023-01-01 00:00:00
end: 2024-01-07 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=2
////////////////////////////////////////////////////////////
//  Copyright by HPotter v1.0 12/01/2017
// This is plots the indicator developed by Andrew Abraham 
// in the Trading the Trend article of TASC September 1998  
//
// You can change long to short in the Input Settings
// Please, use it only for learning or paper trading. Do not for real trading.
////////////////////////////////////////////////////////////
strategy(title="Trend Trader Strategy", overlay = true)
Length = input(21, minval=1),
Multiplier = input(3, minval=1)
reverse = input(false, title="Trade reverse")
avgTR      = wma(atr(1), Length)
highestC   = highest(Length)
lowestC    = lowest(Length)
hiLimit = highestC[1]-(avgTR[1] * Multiplier)
loLimit = lowestC[1]+(avgTR[1] * Multiplier)
ret = iff(close > hiLimit and close > loLimit, hiLimit,
       iff(close < loLimit and close < hiLimit, loLimit, nz(ret[1], 0)))
pos = iff(close > ret, 1,
	   iff(close < ret, -1, nz(pos[1], 0))) 
possig = iff(reverse and pos == 1, -1,
         iff(reverse and pos == -1, 1, pos))	   
if (possig == 1) 
    strategy.entry("Long", strategy.long)
if (possig == -1)
    strategy.entry("Short", strategy.short)	   	    
barcolor(possig == -1 ? red: possig == 1 ? green : blue )
plot(ret, color= blue , title="Trend Trader Strategy")

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-08. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

Build this with AgenKit

A spec is not a trading system. Paste this into your coding agent and AgenKit runs a specialist team — architecture, test-first build, risk gates, review — with paper trading on by default.

in Claude Code, Cursor, Codex & 4 more
/agenkit build a production-grade trading bot with a backtest engine, risk controls and paper trading by default from strategies/Andrew-Abraham-Trend-Tracking-Strategy.md
  1. npx agenkit install engineering-kit in your project
  2. Paste the command into your agent and approve each phase
  3. Get tests, backtest, risk controls and a paper-trading switch
Get AgenKitHow /agenkit works

Understand it first

Before you run this, read the lesson behind the idea and how to backtest it honestly. Specs like this one are the survivors of an unknown number of trials; deflate their results accordingly.

Related specs

esc

Try , , or .

↑↓ move↵ open