ATR and Volatility Index Based Trend Tracking Strategy

Pine ScriptTrend following Volatility EMA ATR

Author: ChaoZhang
Complexity: Intermediate · 72 lines
Parameters: 9
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy uses Average True Range (ATR) and CHOP index as the main technical indicators to identify and track trends. It enters when the index breaks through the upper and lower rails, combined with the trend direction as entry signals; and exits with stop loss or take profit when the index re-enters the belt area.

Strategy Principle

Use ATR to calculate the box size and construct the Renko channel to determine the price trend direction.
Apply the CHOP index to judge if the market is suitable for trading. This index incorporates highest price, lowest price and ATR. When it is between 38.2-61.8, it indicates low market volatility; otherwise, it signals high volatility and unsuitable trading market.
When the CHOP index breaks down the 61.8 upper rail, the price enters a downward trend. If the short-term fast EMA also shows the price is leading, go short. Conversely, when CHOP breaks up the 38.2 lower rail and the short-term EMA rises, go long.
Use stop loss/take profit strategy. When the price re-enters the 38.2-61.8 belt area of CHOP, close the position with stop loss or take profit.

Advantage Analysis

This strategy combines trend judgment and volatility control, which can both capture price trends and control risks. It is a relatively stable trend tracking strategy.

The Renko channel constructed by ATR can effectively track price trends.
The CHOP index judges market volatility rate to avoid incorrect trading in violent fluctuations.
Combining fast EMA to determine short-term trend direction avoids reverse operation.
Stop loss/take profit strategy controls single loss.

Risk Analysis

The main risks this strategy faces:

In sideways market, ATR channel and CHOP signals may produce incorrect signals. Fine tune parameters to filter out wrong signals appropriately.
Single technical indicator combo cannot fully avoid losses. Manual intervention is needed to determine major trends.
Stop loss position set too loose may lead to oversized single loss. Should narrow down stop loss magnitude properly.

Optimization Direction

This strategy can be optimized in the following aspects:

Increase other auxiliary indicators to determine signals, like candlestick patterns, volume etc to improve signal accuracy.
Optimize parameters of ATR and CHOP to capture price fluctuations better.
Set dynamic stop loss/take profit positions for larger profit margins and faster stop loss.
Properly loosen stop loss range after determining major trend to gain more profits in the trend.

Conclusion

This strategy integrates commonly-used technical indicators and is simple & practical. With parameter adjustment and optimization, satisfactory tracking effects can be obtained. But it still requires manual judgment of major trends and cannot be fully automated. It can serve as an auxiliary decision-making tool and reference for other strategies.

Parameters

Name	Default	What it controls
v_input_1	true	Max Intraday Loss(%)
v_input_2	0	Renko Assignment Method: ATR \| Traditional \| Part of Price
v_input_3	14	Value
v_input_4	0	Price Source: Close \| Open / Close \| High / Low
v_input_5	0	Use True Range instead of Volume: Auto \| Always \| Never
v_input_6	false	Oscillating
v_input_7	false	Normalize
v_input_8	14	length
v_input_9	false	Offset

Source Pine Script

/*backtest
start: 2022-12-28 00:00:00
end: 2024-01-03 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © sharatgbhat

//@version=4
strategy("Weis Chop Strategy", overlay=false, default_qty_type = strategy.percent_of_equity, default_qty_value = 10,max_lines_count = 500, max_labels_count = 500)
maxIdLossPcnt = input(1, "Max Intraday Loss(%)", type=input.float)
// strategy.risk.max_intraday_loss(maxIdLossPcnt, strategy.percent_of_equity)

method = input(defval="ATR", options=["ATR", "Traditional", "Part of Price"], title="Renko Assignment Method")
methodvalue = input(defval=14.0, type=input.float, minval=0, title="Value")
pricesource = input(defval="Close", options=["Close", "Open / Close", "High / Low"], title="Price Source")
useClose = pricesource == "Close"
useOpenClose = pricesource == "Open / Close" or useClose
useTrueRange = input(defval="Auto", options=["Always", "Auto", "Never"], title="Use True Range instead of Volume")
isOscillating = input(defval=false, type=input.bool, title="Oscillating")
normalize = input(defval=false, type=input.bool, title="Normalize")
vol = useTrueRange == "Always" or useTrueRange == "Auto" and na(volume) ? tr : volume
op = useClose ? close : open
hi = useOpenClose ? close >= op ? close : op : high
lo = useOpenClose ? close <= op ? close : op : low

if method == "ATR"
    methodvalue := atr(round(methodvalue))
if method == "Part of Price"
    methodvalue := close / methodvalue

currclose = float(na)
prevclose = nz(currclose[1])
prevhigh = prevclose + methodvalue
prevlow = prevclose - methodvalue
currclose := hi > prevhigh ? hi : lo < prevlow ? lo : prevclose

direction = int(na)
direction := currclose > prevclose ? 1 : currclose < prevclose ? -1 : nz(direction[1])
directionHasChanged = change(direction) != 0
directionIsUp = direction > 0
directionIsDown = direction < 0

barcount = 1
barcount := not directionHasChanged and normalize ? barcount[1] + barcount : barcount
vol := not directionHasChanged ? vol[1] + vol : vol
res = barcount > 1 ? vol / barcount : vol

plot(isOscillating and directionIsDown ? -res : res, style=plot.style_columns, color=directionIsUp ? color.green : color.red, transp=75, linewidth=3, title="Wave Volume")

length = input(14, minval=1)
ci = 100 * log10(sum(atr(1), length) / (highest(length) - lowest(length))) / log10(length)
offset = input(0, "Offset", type = input.integer, minval = -500, maxval = 500)
plot(ci, "CHOP", color=#2962FF, offset = offset)
band1 = hline(61.8, "Upper Band", color=#787B86, linestyle=hline.style_dashed)
band0 = hline(38.2, "Lower Band", color=#787B86, linestyle=hline.style_dashed)
fill(band1, band0, color = color.rgb(33, 150, 243, 90), title = "Background")

MomentumBull = close>ema(close,8)
MomentumBear = close<ema(close,8)
Tradecon = crossunder(ci,61.8)

if (MomentumBull and directionIsUp and Tradecon)
	strategy.entry("Buy", strategy.long)
if (MomentumBear and directionIsDown and Tradecon )
    strategy.entry("Sell", strategy.short)
    strategy.exit("exit","Buy",when=directionIsDown,qty_percent=100,profit=20,loss=10)
    strategy.exit("exit","Sell",when=directionIsUp,qty_percent=100,profit=20,loss=10)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-04. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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