Correlation based Bullish Bearish Crypto Trading Strategy Based on Wall Street CCI Index

This is an automated trading strategy that generates long/short/close signals on the target crypto currency based on the calculated trend of a benchmark crypto currency considered correlated with it, using the Wall…

Pine ScriptBreakout Trend following Volatility CCI

Author: ChaoZhang
Complexity: Intermediate · 173 lines
Parameters: 9
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

With default parameters and ETH/USDT as base symbol, the strategy shows good backtest results on symbols like DENT/USDT, BTT/USDT, FTT/USDT, DOT/USDT etc. This makes sense as ETH is quite influential in crypto markets so many cryptos tend to follow ETH's major movements.

Note: The strategy with default parameters is intended for 4h timeframe. On other timeframes, try different support length.

How The Strategy Works

A WMA is calculated on the base symbol, with length 200 by default.
When WMA is rising, go long. When falling go short.
TakeProfit for Long/Short and StopLoss for Long/Short are calculated percentages so 0.05 = 5% etc. Also, TakeProfit/StopLoss are calculated on the base symbol not chart's symbol.
The strategy uses market orders for entry and exit based on following logic:
- When WMA rising and no position, long entry
- When WMA falling and no position, short entry
- When long position profit >= TakeProfitLong percent, close long
- When short position profit >= TakeProfitShort percent, close short
- When long position loss >= StopLossLong percent, close long
- When short position loss >= StopLossShort percent, close short
TakeProfit and StopLoss prices are updated in realtime based on base symbol price changes.

Advantage Analysis

The strategy is highly adaptable for use on multiple crypto currencies by tuning parameters.
Using Wall Street CCI to determine trend avoids noise-led wrong trades. CCI has lag in breakouts helping avoid false breakout losses.
Incorporating TakeProfit and StopLoss allows trend following while controlling loss per trade.
Fully automated trading without manual intervention allows 24/7 runtime.

Risk Analysis

Risk of target crypto price decoupling from base crypto, leading to failure of strategy. Can optimize by using multiple base cryptos and choosing the highest correlated one.
Risk of sudden volatility stopping out positions. Can adjust StopLoss percent or use trailing stops.
Risk of TakeProfit percent too small to capture sufficient trend gains. Can incorporate trend tracking or dynamic take profit.
Risk of false breakout leading to stop loss exit. Can tune CCI parameters or add re-entry logic.

Optimization Directions

Use correlation analysis across multiple base cryptos and combine indicators to reduce single base crypto risk.
Add trend tracking to dynamically adjust TakeProfit/StopLoss based on volatility.
Add staged stops to prevent extreme moves stopping out positions.
Add re-entry logic to avoid missing further trends after stop loss exit.
Optimize CCI parameters and settings to improve signal effectiveness.
Optimize parameters separately for each target crypto to improve adaptability.
Optimize position sizing based on account size.

Summary

Overall this is a typical trend following strategy. The core idea is to determine the trend direction of a benchmark crypto using Wall Street CCI and trade the target crypto accordingly. The strategy has some advantages but also risks to note. Further enhancements in tuning, trend tracking, risk control etc. can improve stability and profitability. In summary, the strategy provides ideas and reference for automated systematic crypto trading.

Parameters

Name	Default	What it controls
v_input_int_1	200	Support Length
v_input_1	BTC_USDT:swap	Correlated Symbol
v_input_2_hlc3	0	Price Source: hlc3 \| high \| low \| open \| hl2 \| close \| hlcc4 \| ohlc4
v_input_float_1	0.2	Take Profit Long
v_input_float_2	0.15	Take Profit Short
v_input_float_3	0.1	Stop Loss Long
v_input_float_4	0.04	Stop Loss Short
v_input_3	timestamp(01 Jan 2016 00:00 +0000)	Start Time
v_input_4	timestamp(31 Dec 2050 23:59 +0000)	End Time

Source Pine Script

/*backtest
start: 2022-10-25 00:00:00
end: 2023-10-31 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/
// © levieux

//@version=5
strategy(title='Correlation Strategy', shorttitle='Correlation Strategy', initial_capital=1000, overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=100, commission_type=strategy.commission.percent, commission_value=0.1)

supportLength = input.int(200, minval=1, title='Support Length')
supportSymbol = input('BTC_USDT:swap', title='Correlated Symbol')
supportSource = input(hlc3, title='Price Source')
takeprofitLong = input.float(0.2, 'Take Profit Long', step=0.01)
takeprofitShort = input.float(0.15, 'Take Profit Short', step=0.01)
stoplossLong = input.float(0.1, 'Stop Loss Long', step=0.01)
stoplossShort = input.float(0.04, 'Stop Loss Short', step=0.01)
start = input(defval = timestamp("01 Jan 2016 00:00 +0000"), title = "Start Time")
end = input(defval = timestamp("31 Dec 2050 23:59 +0000"), title = "End Time")

supportTicker = request.security(supportSymbol, timeframe.period, supportSource, lookahead=barmerge.lookahead_off)  //input(close, title="Source")
supportLine = ta.wma(supportTicker, supportLength)

window() => true

if not window()
    strategy.cancel_all()

supportLongPrice = close
supportShortPrice = close

if strategy.position_size > 0
    supportLongPrice := supportLongPrice[1]
if strategy.position_size < 0
    supportShortPrice := supportShortPrice[1]

longCondition = ta.rising(supportLine, 5) and window() and strategy.position_size <= 0
shortCondition = ta.falling(supportLine, 5) and window() and window() and strategy.position_size > 0
takeprofitLongCondition = takeprofitLong > 0 and window() and strategy.position_size > 0 and supportTicker > supportLongPrice * (1 + takeprofitLong)
stoplossLongCondition = stoplossLong > 0 and window() and strategy.position_size > 0 and supportTicker < supportLongPrice * (1 - stoplossLong)
takeprofitShortCondition = takeprofitShort > 0 and window() and strategy.position_size < 0 and supportTicker > supportShortPrice * (1 + takeprofitShort)
stoplossShortCondition = stoplossShort > 0 and window() and strategy.position_size < 0 and supportTicker < supportShortPrice * (1 - stoplossShort)

if longCondition
    strategy.entry('Long', strategy.long)
    supportLongPrice := supportTicker

if shortCondition
    strategy.entry('Short', strategy.short)
    supportShortPrice := supportTicker

if takeprofitLongCondition
    strategy.close('Long')
if stoplossLongCondition
    strategy.close('Long')
if takeprofitShortCondition
    strategy.close('Short')
if stoplossShortCondition
    strategy.close('Short')

///////////////////
// MONTHLY TABLE //

new_month = month(time) != month(time[1])
new_year  = year(time)  != year(time[1])

eq = strategy.equity

bar_pnl = eq / eq[1] - 1
bar_bh = (close-close[1])/close[1]

cur_month_pnl = 0.0
cur_year_pnl  = 0.0
cur_month_bh = 0.0
cur_year_bh  = 0.0

// Current Monthly P&L
cur_month_pnl := new_month ? 0.0 : 
                 (1 + cur_month_pnl[1]) * (1 + bar_pnl) - 1 
cur_month_bh := new_month ? 0.0 : 
                 (1 + cur_month_bh[1]) * (1 + bar_bh) - 1

// Current Yearly P&L
cur_year_pnl := new_year ? 0.0 : 
                 (1 + cur_year_pnl[1]) * (1 + bar_pnl) - 1
cur_year_bh := new_year ? 0.0 : 
                 (1 + cur_year_bh[1]) * (1 + bar_bh) - 1

// Arrays to store Yearly and Monthly P&Ls
var month_pnl  = array.new_float(0)
var month_time = array.new_int(0)
var month_bh  = array.new_float(0)

var year_pnl  = array.new_float(0)
var year_time = array.new_int(0)
var year_bh  = array.new_float(0)

end_time = false

end_time:= time_close + (time_close - time_close[1]) > timenow or barstate.islastconfirmedhistory

if (not na(cur_month_pnl[1]) and (new_month or end_time))
    if (end_time[1])
        array.pop(month_pnl)
        array.pop(month_time)
        
    array.push(month_pnl , cur_month_pnl[1])
    array.push(month_time, time[1])
    array.push(month_bh , cur_month_bh[1])

if (not na(cur_year_pnl[1]) and (new_year or end_time))
    if (end_time[1])
        array.pop(year_pnl)
        array.pop(year_time)
        
    array.push(year_pnl , cur_year_pnl[1])
    array.push(year_time, time[1])
    array.push(year_bh , cur_year_bh[1])

// Monthly P&L Table    
var monthly_table = table(na)

getCellColor(pnl, bh)  => 
    if pnl > 0
        if bh < 0 or pnl > 2 * bh
            color.new(color.green, transp = 20)
        else if pnl > bh
            color.new(color.green, transp = 50)
        else
            color.new(color.green, transp = 80)
    else
        if bh > 0 or pnl < 2 * bh
            color.new(color.red, transp = 20)
        else if pnl < bh
            color.new(color.red, transp = 50)
        else
            color.new(color.red, transp = 80)

if end_time
    monthly_table := table.new(position.bottom_right, columns = 14, rows = array.size(year_pnl) + 1, border_width = 1)

    table.cell(monthly_table, 0,  0, "",     bgcolor = #cccccc)
    table.cell(monthly_table, 1,  0, "Jan",  bgcolor = #cccccc)
    table.cell(monthly_table, 2,  0, "Feb",  bgcolor = #cccccc)
    table.cell(monthly_table, 3,  0, "Mar",  bgcolor = #cccccc)
    table.cell(monthly_table, 4,  0, "Apr",  bgcolor = #cccccc)
    table.cell(monthly_table, 5,  0, "May",  bgcolor = #cccccc)
    table.cell(monthly_table, 6,  0, "Jun",  bgcolor = #cccccc)
    table.cell(monthly_table, 7,  0, "Jul",  bgcolor = #cccccc)
    table.cell(monthly_table, 8,  0, "Aug",  bgcolor = #cccccc)
    table.cell(monthly_table, 9,  0, "Sep",  bgcolor = #cccccc)
    table.cell(monthly_table, 10, 0, "Oct",  bgcolor = #cccccc)
    table.cell(monthly_table, 11, 0, "Nov",  bgcolor = #cccccc)
    table.cell(monthly_table, 12, 0, "Dec",  bgcolor = #cccccc)
    table.cell(monthly_table, 13, 0, "Year", bgcolor = #999999)


    for yi = 0 to array.size(year_pnl) - 1
        table.cell(monthly_table, 0,  yi + 1, str.tostring(year(array.get(year_time, yi))), bgcolor = #cccccc)
        
        y_color = getCellColor(array.get(year_pnl, yi), array.get(year_bh, yi))
        table.cell(monthly_table, 13, yi + 1, str.tostring(math.round(array.get(year_pnl, yi) * 100)) + " (" + str.tostring(math.round(array.get(year_bh, yi) * 100)) + ")", bgcolor = y_color)
        
    for mi = 0 to array.size(month_time) - 1
        m_row   = year(array.get(month_time, mi))  - year(array.get(year_time, 0)) + 1
        m_col   = month(array.get(month_time, mi)) 
        m_color = getCellColor(array.get(month_pnl, mi), array.get(month_bh, mi))
        
        table.cell(monthly_table, m_col, m_row, str.tostring(math.round(array.get(month_pnl, mi) * 100)) + " (" + str.tostring(math.round(array.get(month_bh, mi) * 100)) +")", bgcolor = m_color)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-11-01. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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