ADX Dynamic Trend Strategy

Pine ScriptTrend following Breakout RSI EMA SMA ADX / DMI

Author: ChaoZhang
Complexity: Intermediate · 80 lines
Parameters: 13
Backtest bar: 10m · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

The ADX Dynamic Trend Strategy is a quantitative trading strategy that utilizes the ADX indicator to determine the strength and direction of market trends. It generates buy and sell signals by calculating the Average Directional Index (ADX) to judge if a trend exists in the market and by calculating the Positive Directional Indicator (DI+) and Negative Directional Indicator (DI-) to determine the direction of the trend.

Trading Logic

The strategy first uses the ADX indicator to determine if a trend exists in the market. When ADX is above a user-defined key level (default 23), it signals that the market trend is relatively strong. When the current ADX value is higher than the ADX value n days ago (n is the user-defined lookback period, default 3 days), it signals that ADX is rising and a trend is forming in the market.

The strategy then utilizes DI+ and DI- to determine the direction of the market trend. When DI+ is higher than DI-, it signals an uptrend in the market. When DI+ is lower than DI-, it signals a downtrend in the market.

Finally, the strategy combines the ADX and DI analysis to generate specific buy and sell signals:

When ADX rises and is above key level and DI+ is higher than DI-, a buy signal is generated
When ADX rises and is above key level and DI+ is lower than DI-, a sell signal is generated
When ADX turns to decrease, a flatten position signal is generated

The strategy also provides features like moving average filtering and customizable backtesting time range.

Advantage Analysis

The ADX Dynamic Trend Strategy has the following advantages:

Automatically detect the existence of market trends, avoiding ineffective trading
Automatically determine the direction of market trends for trend following
Clear logic of buying on trend existence and flattening on trend disappearance
Configurable moving average filtering avoids false breakouts
Customizable backtesting time range for historical testing
Adjustable indicator parameters for optimization across different products

Risk Analysis

The strategy also has some risks:

ADX indicator has lagging effect, possibly missing early trend opportunities
Trend direction reliance on DI may produce false signals as DI is sensitive
Moving average filter may miss short-term opportunities
Improper backtesting time frame may cause overfitting
Improper indicator parameters may affect strategy performance

To mitigate risks, the following can be considered:

Shorten ADX parameters to reduce lagging
Remove or adjust DI filter to prevent false signals
Shorten moving average period
Expand backtesting time frame for full sample testing
Optimize parameters to find best settings

Enhancement Opportunities

The strategy can be enhanced from the following aspects:

Portfolio testing across multiple stocks to diversify single-stock risk
Add stop loss logic to control per trade loss
Combine with other indicators for signal verification to improve accuracy
Introduce machine learning algorithms for buy/sell signal generation
Add auto parameter tuning module for dynamic adjustment

Conclusion

The ADX Dynamic Trend Strategy utilizes ADX to determine trend existence and DI for trend direction. It generates trading signals when a trend exists and flattens positions when the trend disappears. The logic is clear. By automatically detecting and tracking trends, ineffective trading can be avoided to some extent in non-trending markets. With proper enhancement, this strategy can become a powerful tool for medium-to-long term quantitative trading.

Parameters

Name	Default	What it controls
v_input_1	2019	From Year
v_input_2	true	From Month
v_input_3	true	From Day
v_input_4	9999	To Year
v_input_5	true	To Month
v_input_6	true	To Day
v_input_7	14	ADX Smoothing
v_input_8	14	DI Period
v_input_9	23	Keylevel for ADX
v_input_10	3	Lookback Period for Slope
v_input_11	true	Use MA for Filtering?
v_input_12	0	MA Type For Filtering: EMA \| SMA
v_input_13	200	MA Period for Filtering

Source Pine Script

/*backtest
start: 2024-01-07 00:00:00
end: 2024-01-14 00:00:00
period: 10m
basePeriod: 1m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © millerrh with inspiration from @9e52f12edd034d28bdd5544e7ff92e 
//The intent behind this study is to look at ADX when it has an increasing slope and is above a user-defined key level (23 default). 
//This is to identify when it is trending.
//It then looks at the DMI levels.  If D+ is above D- and the ADX is sloping upwards and above the key level, it triggers a buy condition.  Opposite for short.
//Can use a user-defined moving average to filter long/short if desried.
// NOTE: THIS IS MEANT TO BE USED IN CONJUNCTION WITH MY "ATX TRIGGER" INDICATOR FOR VISUALIZATION. MAKE SURE SETTINGS ARE THE SAME FOR BOTH.

strategy("ADX | DMI Trend", overlay=true, initial_capital=10000, currency='USD', 
   default_qty_type=strategy.percent_of_equity, default_qty_value=100, commission_type=strategy.commission.percent, commission_value=0.04)

// === BACKTEST RANGE ===
From_Year  = input(defval = 2019, title = "From Year")
From_Month = input(defval = 1, title = "From Month", minval = 1, maxval = 12)
From_Day   = input(defval = 1, title = "From Day", minval = 1, maxval = 31)
To_Year    = input(defval = 9999, title = "To Year")
To_Month   = input(defval = 1, title = "To Month", minval = 1, maxval = 12)
To_Day     = input(defval = 1, title = "To Day", minval = 1, maxval = 31)
Start  = timestamp(From_Year, From_Month, From_Day, 00, 00)  // backtest start window
Finish = timestamp(To_Year, To_Month, To_Day, 23, 59)        // backtest finish window

// == INPUTS ==
// ADX Info
adxlen = input(14, title="ADX Smoothing")
dilen = input(14, title="DI Period")
keyLevel = input(23, title="Keylevel for ADX")
adxLookback = input(3, title="Lookback Period for Slope")

// == FILTERING ==
// Inputs
useMaFilter = input(title = "Use MA for Filtering?", type = input.bool, defval = true)
maType = input(defval="EMA", options=["EMA", "SMA"], title = "MA Type For Filtering")
maLength   = input(defval = 200, title = "MA Period for Filtering", minval = 1)

// Declare function to be able to swap out EMA/SMA
ma(maType, src, length) =>
    maType == "EMA" ? ema(src, length) : sma(src, length) //Ternary Operator (if maType equals EMA, then do ema calc, else do sma calc)
maFilter = ma(maType, close, maLength)
plot(maFilter, title = "Trend Filter MA", color = color.green, linewidth = 3, style = plot.style_line, transp = 50)

// Check to see if the useMaFilter check box is checked, this then inputs this conditional "maFilterCheck" variable into the strategy entry 
maFilterCheck = if useMaFilter == true
    maFilter
else
    close

// == USE BUILT-IN DMI FUNCTION TO DETERMINE ADX AND BULL/BEAR STRENGTH
[diplus, diminus, adx] = dmi(dilen, adxlen)

buySignal = (adx[0]-adx[adxLookback] > 0) and adx > keyLevel and diplus > diminus  and close >= maFilterCheck
// buySignalValue = valuewhen(buySignal, close, 0)
shortSignal = (adx[0]-adx[adxLookback] > 0) and adx > keyLevel and diplus < diminus  and close <= maFilterCheck
// shortSignalValue = valuewhen(shortSignal, close, 0)
sellCoverSignal = adx[0]-adx[adxLookback] < 0

// == ENTRY & EXIT CRITERIA
// Triggers to be TRUE for it to fire of the BUY Signal : (opposite for the SELL signal).
// (1): Price is over the 200 EMA line. (EMA level configurable by the user)
// (2): "D+" is OVER the "D-" line
// (3): RSI 7 is under 30 (for SELL, RSI 7 is over 70)
// 1* = The ultimate is to have a combination line of 3 EMA values, EMA 14, EMA 50 and EMA 200 - And if price is over this "combo" line, then it's a strong signal

// == STRATEGY ENTRIES/EXITS == 
strategy.entry("Long", strategy.long, when = buySignal)
strategy.close("Long", when = sellCoverSignal)
strategy.entry("Short", strategy.short, when = shortSignal)
strategy.close("Short", when = sellCoverSignal)
    
// == ALERTS == 
// alertcondition(buySignal, title='ADX Trigger Buy', message='ADX Trigger Buy')
// alertcondition(sellSignal, title='ADX Trigger Sell', message='ADX Trigger Sell')

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-15. 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/ADX-Dynamic-Trend-Strategy.md

npx agenkit install engineering-kit in your project
Paste the command into your agent and approve each phase
Get tests, backtest, risk controls and a paper-trading switch

Get AgenKit How /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.