Patient Trend Following Strategy

The Patient Trend Following Strategy is a trend following strategy. It uses a combination of moving averages to determine the trend direction and CCI oscillator to generate trading signals. This strategy pursues big…

Pine ScriptTrend following Machine learning Mean reversion EMA SMA ATR Supertrend CCI

Author: ChaoZhang
Complexity: Intermediate · 164 lines
Parameters: 22
Backtest bar: 1m · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

The strategy uses 21-period and 55-period EMA combination to define the trend direction. An uptrend is defined when the short EMA is above the long EMA. A downtrend is defined when the short EMA is below the long EMA.

The CCI indicator is used to detect overbought and oversold situations. CCI crossing above -100 signals bottom oversold condition and crossing below 100 signals top overbought condition. Different overbought and oversold levels of CCI produce trading signals with different confidence levels.

When an uptrend is determined, strong bottom oversold signals from CCI will trigger long entry orders. When a downtrend is determined, strong top overbought signals from CCI will trigger short entry orders.

The stop loss is set at SuperTrend lines. Take profit is fixed number of pips.

Advantage Analysis

The main advantages of this strategy are:

Following big trends, avoiding whipsaws
CCI effectively detects reversal points
Reasonable stop loss setting with SuperTrend
Fixed stop loss and take profit, controllable risk

Risk Analysis

The main risks of this strategy are:

Probability of incorrect big trend determination
Probability of false signals from CCI
Probability of unnecessary stop loss with improper stop loss levels
Probability of missing trend profits with fixed take profit

To cope with these risks, parameters like EMA periods, CCI periods and stop loss/take profit levels can be optimized. Introducing more indicators for signal verification is also necessary.

Optimization Directions

The main optimization directions are:

Test more indicator combinations to find better trend and signal verification indicators.
Utilize dynamic stop loss and take profit with ATR to better follow trends and control risk.
Introduce machine learning models trained on historical data to judge trend probabilities.
Optimize parameters for different trading instruments.

Conclusion

The Patient Trend Following Strategy is a very practical trend trading strategy overall. It defines big trend with moving averages and detects reversal signals with CCI oscillator, while setting reasonable stop loss levels using SuperTrend indicator. With further parameter tuning and more indicator combinations for signal verification, this strategy can be further optimized and is worth tracking in live trading.

Parameters

Name	Default	What it controls
v_input_1	21	Short EMA
v_input_2	55	Long EMA
v_input_3	20	Overbought/sold detector period
v_input_4_close	0	Overbought/sold detector source: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_5	100	Overbought level 1
v_input_6	140	Overbought level 2
v_input_7	180	Overbought level 3
v_input_8	-100	Oversold level 1
v_input_9	-140	Oversold level 2
v_input_10	-180	Oversold level 3
v_input_11	55	ATR Period
v_input_12	5	ATR Multiplier
v_input_13	true	Take Wicks into Account ?
v_input_14	true	Illuminate Trend
v_input_15	0.0002	Spread
v_input_16	true	Test longs
v_input_17	true	Test shorts
v_input_18	true	Test level 1 overbought/sold levels
v_input_19	false	Test level 2 overbought/sold levels
v_input_20	false	Test level 3 overbought/sold levels
v_input_21	true	Use static target
v_input_22	400	Static target in pips

Source Pine Script

/*backtest
start: 2024-01-10 00:00:00
end: 2024-01-17 00:00:00
period: 1m
basePeriod: 1m
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/
// © greenmask9

//@version=4
strategy("Patient Trendfollower (7) Strategy", overlay=true)

// 21 EMA
emalength = input(21, title="Short EMA")
emashort = ema(close, emalength)
plot(emashort, color = color.purple, linewidth=1)

// 55 EMA
emalength2 = input(55, title="Long EMA")
ema = ema(close, emalength2)
plot(ema, color = color.green, linewidth=1)

//CCI calculation and inputs
lengthcci = input(20, minval=1, title="Overbought/sold detector period")
src = input(close, title="Overbought/sold detector source")
ma = sma(src, lengthcci)
ccivalue = (src - ma) / (0.015 * dev(src, lengthcci))


//CCI plotting
ccioverbought = input(defval=100, title="Overbought level 1")
ccioverbought2 = input(defval=140, title="Overbought level 2")
ccioverbought3 = input(defval=180, title="Overbought level 3")

ccioversold = input(defval=-100, title="Oversold level 1")
ccioversold2 = input(defval=-140, title="Oversold level 2")
ccioversold3 = input(defval=-180, title="Oversold level 3")

cciOB = (ccivalue >= ccioverbought and ccivalue < ccioverbought2)
plotshape(cciOB,  title= "Overbought", location=location.abovebar, color=color.lime, transp=0, style=shape.circle)
cciOS = (ccivalue <= ccioversold and ccivalue > ccioversold2)
plotshape(cciOS, title= "Oversold", location=location.belowbar, color=color.lime, transp=0, style=shape.circle)

cciOB2 = (ccivalue >= ccioverbought2 and ccivalue < ccioverbought3)
plotshape(cciOB2,  title= "Overbought", location=location.abovebar, color=color.red, transp=0, style=shape.circle)
cciOS2 = (ccivalue <= ccioversold and ccivalue > ccioversold3)
plotshape(cciOS2, title= "Oversold", location=location.belowbar, color=color.red, transp=0, style=shape.circle)

cciOB3 = (ccivalue >= ccioverbought3)
plotshape(cciOB3,  title= "Overbought", location=location.abovebar, color=color.black, transp=0, style=shape.circle)
cciOS3 = (ccivalue <= ccioversold3)
plotshape(cciOS3, title= "Oversold", location=location.belowbar, color=color.black, transp=0, style=shape.circle)

//Supertrend

length = input(title="ATR Period", type=input.integer, defval=55)
mult = input(title="ATR Multiplier", type=input.float, step=0.1, defval=5.0)
wicks = input(title="Take Wicks into Account ?", type=input.bool, defval=true)
illuminate = input(title="Illuminate Trend", type=input.bool, defval=true)

atr = mult * atr(length)

longStop = hl2 - atr
longStopPrev = nz(longStop[1], longStop)
longStop := (wicks ? low[1] : close[1]) > longStopPrev ? max(longStop, longStopPrev) : longStop

shortStop = hl2 + atr
shortStopPrev = nz(shortStop[1], shortStop)
shortStop := (wicks ? high[1] : close[1]) < shortStopPrev ? min(shortStop, shortStopPrev) : shortStop

dir = 1
dir := nz(dir[1], dir)
dir := dir == -1 and (wicks ? high : close) > shortStopPrev ? 1 : dir == 1 and (wicks ? low : close) < longStopPrev ? -1 : dir

longColor = color.new(color.green, 90)
shortColor = color.new(color.red, 90)
noneColor = color.new(color.white, 100)

longStopPlot = plot(dir == 1 ? longStop : na, title="Long Stop", style=plot.style_linebr, linewidth=2, color=longColor)
shortStopPlot = plot(dir == 1 ? na : shortStop, title="Short Stop", style=plot.style_linebr, linewidth=2, color=shortColor)
midPricePlot = plot(ohlc4, title="", style=plot.style_circles, linewidth=0)

longFillColor = illuminate ? (dir == 1 ? longColor : noneColor) : noneColor
shortFillColor = illuminate ? (dir == -1 ? shortColor : noneColor) : noneColor
fill(midPricePlot, longStopPlot, title="Long State Filling", color=longFillColor)
fill(midPricePlot, shortStopPlot, title="Short State Filling", color=shortFillColor)

//entries
uptrend = emashort>ema and dir == 1
upsignal = ccivalue<=ccioversold and ccivalue>ccioversold2
upsignal2 = ccivalue<=ccioversold2 and ccivalue>ccioversold3
upsignal3 = ccivalue<=ccioversold3
downtrend = emashort<ema and dir == -1
downsignal = ccivalue>=ccioverbought and ccivalue<ccioverbought2
downsignal2 = ccivalue>=ccioverbought2 and ccivalue<ccioverbought3
downsignal3 = ccivalue>=ccioverbought3

//adapts to the current bar, I need to save the bars number when the condition for buy was true, static number is spread
spread = input (0.00020, title="Spread")
upstoploss = longStop - spread
downstoploss = shortStop + spread
strategy.initial_capital = 50000
ordersize=floor(strategy.initial_capital/close)
testlong = input(title="Test longs", type=input.bool, defval=true)
testshort = input(title="Test shorts", type=input.bool, defval=true)
//new
degree = input(title="Test level 1 overbought/sold levels", type=input.bool, defval=true)
degree2 = input(title="Test level 2 overbought/sold levels", type=input.bool, defval=false)
degree3 = input(title="Test level 3 overbought/sold levels", type=input.bool, defval=false)

statictarget = input(title="Use static target", type=input.bool, defval=true)
statictargetvalue = input(title="Static target in pips", type=input.integer, defval=400)

//timetrade = input(title="Open trades only withing specified time", type=input.bool, defval=true)
//timtrade = input()

//přidat možnost TP podle ATR a sl podle ATR
buy1 = uptrend and upsignal and strategy.opentrades==0 and testlong and degree
x1 = barssince (buy1)
if (buy1)
//bodlo by zakázat atrtarget v tomto případě
    if (statictarget)
        strategy.entry("Long1", strategy.long, ordersize)
        strategy.exit( "Exitlong", from_entry="Long1" , profit=statictargetvalue,stop=upstoploss[x1])
 
buy2 = uptrend and upsignal2 and strategy.opentrades==0 and testlong and degree2
x2 = barssince (buy2)
if (buy2)
//bodlo by zakázat atrtarget v tomto případě
    if (statictarget)
        strategy.entry("Long2", strategy.long, ordersize)
        strategy.exit( "Exitlong", from_entry="Long2" , profit=statictargetvalue,stop=upstoploss[x2])
  
buy3 = uptrend and upsignal3 and strategy.opentrades==0 and testlong and degree3
x3 = barssince (buy3)
if (buy3)
//bodlo by zakázat atrtarget v tomto případě
    if (statictarget)
        strategy.entry("Long3", strategy.long, ordersize)
        strategy.exit( "Exitlong", from_entry="Long3" , profit=statictargetvalue,stop=upstoploss[x3])

sell1 = downtrend and downsignal and strategy.opentrades==0 and testshort and degree
y1 = barssince (sell1)
if (sell1)
    if (statictarget)
        strategy.entry("Sell1", strategy.short, ordersize)
        strategy.exit( "Exitshort", from_entry="Sell1" , profit=statictargetvalue,stop=downstoploss[y1])

sell2 = downtrend and downsignal2 and strategy.opentrades==0 and testshort and degree2
y2 = barssince (sell2)
if (sell2)
    if (statictarget)
        strategy.entry("Sell2", strategy.short, ordersize)
        strategy.exit( "Exitshort", from_entry="Sell2" , profit=statictargetvalue,stop=downstoploss[y2])

sell3 = downtrend and downsignal3 and strategy.opentrades==0 and testshort and degree3
y3 = barssince (sell3)
if (sell3)
    if (statictarget)
        strategy.entry("Sell3", strategy.short, ordersize)
        strategy.exit( "Exitshort", from_entry="Sell3" , profit=statictargetvalue,stop=downstoploss[y3])

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-18. 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/Patient-Trend-Following-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.