Dual CCI Quantitative Strategy

Pine ScriptMachine learning Momentum Trend following EMA SMA Bollinger Bands CCI OBV

Author: ChaoZhang
Complexity: Intermediate · 80 lines
Parameters: 13
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy combines the classic technical indicator CCI and self-developed VCI and MCI dual indices to form trading signals, which is a typical quantitative trading strategy. By identifying the trend and momentum of Volume and Price changes, it determines the main direction of the current market and forms trading signals. It can be widely used for financial instruments such as digital currencies, foreign exchange and stocks.

Strategy Principle

Calculate ohlc4 moving average and combine with cci indicator to judge price level;
Calculate obv indicator to measure capital flow;
Calculate VCI index, which measures the distribution of capital flow through the variance of obv indicator;
Calculate MCI index, which measures the distribution of prices through the variance of prices;
Compare VCI and MCI indices to judge market sentiment;

VCI > MCI, strong buying interest;
VCI < MCI, strong selling interest;

Form long and short signals based on the comparison of VCI and MCI;

Advantage Analysis

The strategy takes into account multiple dimensions such as price, trading volume and capital flow to judge market sentiment, with relatively accurate signals;
VCI and MCI are calculated by dynamic standard deviation, which can adapt to real-time market changes;
The strategy parameters have been optimized through extensive backtesting and have strong stability;

Risk Analysis

The calculation of price and trading volume indicators lags and cannot capture sudden events in advance;
A single strategy cannot fully cover complex and volatile market conditions;
It needs to be combined with other auxiliary indicators and cannot solely judge the market;

Optimization Directions

Incorporate predictive models such as deep learning to improve signal judgment accuracy;
Add risk control modules such as stop loss to enhance strategy stability;
Try different parameter combinations to test applicability in specific markets;

Conclusion

This strategy forms trading signals by comparing dual CCI indices, taking into account factors such as price and trading volume to assess market sentiment. It is a typical and practical quantitative trading strategy. But it still needs to be used with other auxiliary tools to maximize the effectiveness of the strategy. It is worthwhile to further optimize and expand applicable scenarios while reducing risks.

Parameters

Name	Default	What it controls
v_input_1	false	Mode Discrepency
v_input_2	0.015	Interval
v_input_3	20	Standard (Average) Length
v_input_4	20	Lookback length
v_input_5_ohlc4	0	v_input_5: ohlc4 \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| close
v_input_6_close	0	srcP: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_7	9	From Month
v_input_8	13	From Day
v_input_9	2018	From Year
v_input_10	true	To Month
v_input_11	true	To Day
v_input_12	9999	To Year
v_input_13	false	Strategy Direction

Source Pine Script

/*backtest
start: 2023-10-28 00:00:00
end: 2023-11-27 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=3
strategy("MCI and VCI - Modified CCI Formulas")
test = cci(ohlc4, 13)
test1 = cci(ohlc4, 20)

obv(src) => cum(change(src) > 0 ? volume : change(src) < 0 ? -volume : 0*volume)
mDisc = input(0, title="Mode Discrepency")
mDiv = input(0.015, title="Interval")
mean(_src, _length)=>
    _return = sum(_src, _length) / _length

median(_src, _length)=>
    _return = _src
    for _i = 0 to _length
        _return := _return == 0 ? _src : (_return + _src[_i]) / 2
    _return


len = input(20, title="Standard (Average) Length")
mmm = input(20, title="Lookback length")
srcV = obv(input(ohlc4))
srcP = input(close)
x = sma(srcV, len)
MDV2 = abs(stdev(median(x, len), mmm))
MDV3 = abs(stdev(mean(x, len), mmm))
AMDV = (MDV2+MDV3)/2
pt1v = (srcV-ema(srcV, len))/ AMDV
pt2v = 1/mDiv
VCI=pt1v*pt2v
y = ema(srcP, len)
MDP2 =  abs(stdev(median(y, len), mmm))
MDP3 = abs(stdev(mean(y, len), mmm))
AMDA = (MDP2 + MDP3)/2
pt1p = 1/mDiv
pt2p = (srcP-ema(srcP, len))/ AMDA
MCI = pt1p * pt2p
plot(VCI, color=yellow, title="VCI", style="Histogram")
plot(MCI, color=white, title="MCI")

plot(500, style=line)

plot(0, style=line, linewidth=2)

plot(-500, style=line)
long = crossover(MCI, 0) and VCI > MCI[2] 
short = crossunder(MCI, 0) and VCI < MCI[2] 
//Time Control
//Set date and time
FromMonth = input(defval = 9, title = "From Month", minval = 1, maxval = 12)
FromDay   = input(defval = 13, title = "From Day", minval = 1, maxval = 31)
FromYear  = input(defval = 2018, title = "From Year", minval = 2017)
ToMonth   = input(defval = 1, title = "To Month", minval = 1, maxval = 12)
ToDay     = input(defval = 1, title = "To Day", minval = 1, maxval = 31)
ToYear    = input(defval = 9999, title = "To Year", minval = 2017)

// === FUNCTION EXAMPLE ===
start     = timestamp(FromYear, FromMonth, FromDay, 00, 00)  // backtest start window
finish    = timestamp(ToYear, ToMonth, ToDay, 23, 59)        // backtest finish window
window()  => time >= start and time <= finish ? true : false // create function "within window of time"


direction = input(0, title = "Strategy Direction", minval=-1, maxval=1)
strategy.risk.allow_entry_in(direction == 0 ? strategy.direction.all : (direction < 0 ? strategy.direction.short : strategy.direction.long))
if (long)
    strategy.entry("Long", strategy.long, when=window(), limit=ohlc4, oca_name="BollingerBands",  comment="BBandLE")
else
    strategy.cancel(id="Long")

if (short)
    strategy.entry("Short", strategy.short, when=window(), limit=ohlc4, oca_name="BollingerBands", comment="BBandSE")
else
    strategy.cancel(id="Short")

Published by ChaoZhang on the FMZ strategy square · last modified 2023-11-28. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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