CCI Dual Timeframe Trend Following Strategy

Pine ScriptTrend following Machine learning Breakout EMA Ichimoku CCI

Author: ChaoZhang
Complexity: Intermediate · 95 lines
Parameters: 13
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy is a trend following strategy based on the CCI indicator. It generates trading signals by monitoring the crossover between two CCIs of different timeframes. Specifically, it will detect if a shorter period CCI breaks through a longer period CCI and determine long or short positions based on the breakthrough direction.

Strategy Logic

The core logic of this strategy is:

Define two CCIs, ci1 as 14 periods, ci2 as 56 periods
When ci1 breaks above ci2, go long
When ci1 breaks below ci2, go short
Use the values of ci1 and ci2 to determine exits after signals triggered

Specific long rules:

ci1 breaks above ci2, the shorter period CCI above longer period CCI
Stop loss condition: ci1 <-50 and change rate < 0 or ci1 breaks below -100

Specific short rules:

ci1 breaks below ci2, the shorter period CCI below longer period CCI
Stop loss condition: ci1 > 100 and change rate > 0 or ci2 breaks above 100

As we can see, this strategy takes advantage of the sensitivity of shorter period CCI and the stability of longer period CCI to identify and follow trends.

Advantages

The advantages of this strategy:

Effectively identifies trends using the strength of CCI indicator
Dual CCI design filters some noise trades
The combination of long and short period CCIs controls risk while following trends
Simple and clear strategy rules, easy to understand and implement
Highly configurable, both CCI periods and stop loss conditions are customizable

Risks

There are also some risks:

Weak ability to identify range-bound and volatile markets using CCI
Divergence may happen between long and short period CCIs, causing wrong signals
Improper stop loss setting may lead to huge loss
Inappropriate parameter tuning also largely impacts strategy profitability

Solutions:

Incorporate other indicators to determine market condition, avoid trading in volatile period
Add filters to avoid errors from CCI divergence
Optimize and test different stop loss levels
Find suitable parameter sets through backtesting and tuning

Optimization Directions

Areas that the strategy can be further optimized:

Add more indicators to build a more systematic trading system
Test profitability difference between weekdays and sessions
Search for better parameters using machine learning
Tune parameters for different products
Optimize entry and exit rules

Conclusion

In conclusion, this is a simple trend following strategy based on CCI crossover. It can effectively identify trend direction and follow trends. Meanwhile it controls risk via stop loss. This strategy is simple, practical, flexible in parameter tuning, and can serve as a starter quant strategy. It can be enhanced into more powerful system via further optimization and combination.

Parameters

Name	Default	What it controls
v_input_1	14	shortlength
v_input_2	56	longlength
v_input_3	2	aa
v_input_4	75	Ss
v_input_5	10	len
v_input_6	9	lenTurn
v_input_7	26	lenStd
v_input_8	true	from day
v_input_9	true	from month
v_input_10	2019	from yr
v_input_11	13	to day
v_input_12	12	to month
v_input_13	2019	to yr

Source Pine Script

/*backtest
start: 2023-10-24 00:00:00
end: 2023-11-23 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=3
strategy(title="my work",calc_on_order_fills=true,currency=currency.USD, default_qty_type=strategy.percent_of_equity,commission_type=strategy.commission.percent)


source = close
shortlength=input(14)
longlength=input(56)
aa=input(2)
Ss=input(75)

//Cci part
ci1=cci(source,shortlength)   //4시간봉의 기본 cci
ci2=cci(source,longlength)   //4시간봉에서 12시봉의 cci 무빙측정

//오린간 선생님의 WT + ichimoku
len = input(10)
lenTurn = input(9)
lenStd = input(26)

wtm_e(so, l) =>
    esa = ema(so, l)
    d = ema(abs(so - esa), l)
    ci = (so - esa) / (0.015 * d)
    ema(ci, l*2+1)

alh(len) => avg(lowest(len), highest(len))
alh_src(src, len) => avg(lowest(src, len), highest(src, len))

wt = wtm_e(close,len)
turn = alh_src(wt, lenTurn)
std = alh_src(wt, lenStd)

cnt = 0
if wt > turn
    cnt:=cnt+1
if wt > std
    cnt:=cnt+1


//100,-100선
h0 = hline(100)
h1 = hline(-100)

//plot(ci,color=green)
// plot(k,color=green)
// plot(d,color=red)
plot(ci1,color=green)
plot(ci2,color=red)

plot(0,color=black)
plot(100,color=black)
plot(-100,color=black)

fill(h0,h1,color=purple,transp=95)

bgcolor(cnt==0 ? red : cnt==1 ? blue : cnt == 2 ? green : na, transp = Ss)

//기간조정

Fromday = input(defval=1, title="from day", minval=1, maxval=31)
FromMonth = input(defval=1, title="from month", minval=1, maxval=12)
FromYr = input(defval=2019, title="from yr", minval=1970)

Today = input(defval=13, title="to day", minval=1, maxval=31)
ToMonth = input(defval=12, title="to month", minval=1, maxval=12)
ToYr = input(defval=2019, title="to yr", minval=1970)

startDate = timestamp(FromYr, FromMonth, Fromday, 00, 00)
finishDate = timestamp(ToYr, ToMonth, Today, 00, 00)
Time_cond = true


/////롱

if  crossover(ci1,ci2) and change(ci2)>0 and Time_cond
    strategy.entry("go", strategy.long, comment="go")
    
strategy.close("go", (ci2<0 and ci1 <-50 and change(ci1)<0) or (crossunder(ci1,-100) and strategy.openprofit<0) and change(cnt)<0)



/////숏

if  (crossunder(ci1,ci2) and change(ci2)<0 and falling(ci1,aa)) and Time_cond
    strategy.entry("die", strategy.short, comment="die")
    
strategy.close("die", (ci2>0 and ci1 > 100 and change(ci1)>0) or (crossover(ci2,100) and strategy.openprofit<0) and change(cnt)>0)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-11-24. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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