The Breakthrough Callback Trading Strategy

Pine ScriptBreakout Mean reversion Trend following MACD EMA SMA

Author: ChaoZhang
Complexity: Intermediate · 56 lines
Parameters: 10
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

The breakthrough callback trading strategy realizes breakthrough callback trading under specific trends by calculating the absolute strength index and MACD index of prices. It belongs to short-term trading strategies. This strategy integrates multiple indicators to judge major trends, medium-term trends and short-term trends. It conducts trend tracking transactions through trend-aligned and indicator-complementary confirmation signals.

Strategy Principle

This strategy mainly relies on the absolute strength index and MACD index of prices to implement breakthrough callback trading. Firstly, it calculates the 9-period, 21-period and 50-period EMAs of prices to judge the major trend direction; then it calculates the absolute strength index of prices to reflect the strength of short-term adjustments; finally it calculates the MACD index to judge the short-term trend direction. It buys when the major trend is upward and there is a short-term adjustment; it sells when the major trend is downward and there is a short-term rebound.

Specifically, the major upward trend of the variety requires the 9-day EMA to be higher than the 21-day EMA, and the 21-day EMA to be higher than the 50-day EMA. The criteria for judging short-term adjustments are that the difference of the absolute strength index is less than 0 and MACDDIFF is less than 0. The major downward trend of the variety requires the 9-day EMA to be lower than the 21-day EMA, and the 21-day EMA to be lower than the 50-day EMA. The criteria for judging short-term rebounds are that the difference of the absolute strength index is greater than 0 and MACDDIFF is greater than 0.

Advantage Analysis

The strategy has the following advantages:

Combining major trends and short-term adjustments to avoid false breakouts
Higher reliability with combination of multiple indicators
The absolute strength index reflects the strength of adjustments to judge the quality of callbacks
MACD can judge short-term trends and overbought/oversold areas

Risk Analysis

The strategy also has some risks:

Wrong judgement of major trends may lead to trade failure
Wrong judgement of callback time and strength may lead to invalid callback
Divergence of indicators in extreme market conditions, resulting in wrong signals

In response to the above risks, methods such as optimizing parameters, judging indicators of different cycles, adjusting position rules to control single loss, combining more indicators to filter signals, and improving accuracy can be used to improve the strategy.

Optimization Directions

The strategy can be optimized in the following aspects:

Test more indicator combinations to find more suitable trading strategies
Optimize indicator parameters to improve indicator sensitivity
Adjust stop loss methods to reduce maximum single loss
Increase filtering conditions to issue signals in more effective areas
Combine more time frame indicators to improve judgement accuracy

Summary

In summary, the breakthrough callback trading strategy is generally a relatively stable short-term trading strategy. It combines multi-timeframe trend judgments to avoid erroneous transactions in oscillating markets. At the same time, the combined use of indicators also improves the accuracy of judgments. Through subsequent testing and optimization, this strategy can become a stable strategy worth holding for the long term.

Parameters

Name	Default	What it controls
v_input_1	long entry message	message_long_entry
v_input_2	long exit message	message_long_exit
v_input_3	short entry message	message_short_entry
v_input_4	short exit message	message_short_exit
v_input_5	23	Fast Length
v_input_6	11	Slow Length
v_input_7_open	0	Source: open \| high \| low \| close \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_int_1	6	Signal Smoothing
v_input_string_1	0	Oscillator MA Type: EMA \| SMA
v_input_string_2	0	Signal Line MA Type: SMA \| EMA

Source Pine Script

/*backtest
start: 2024-01-01 00:00:00
end: 2024-01-31 23:59:59
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5 
strategy("Divergence Scalper [30MIN]", overlay=true , commission_value=0.04 ) 
message_long_entry = input("long entry message") 
message_long_exit = input("long exit message") 
message_short_entry = input("short entry message") 
message_short_exit = input("short exit message") 
//3x ema 
out9 = ta.ema(close,9) 
out21 = ta.ema(close,21) 
out50 = ta.ema(close,50) 
//abs 
absolute_str_formula( ) => 
    top=0.0 
    bottom=0.0 
    if(close>close[1]) 
        top:= nz(top[1])+(close/close[1]) 
    else 
        top:=nz(top[1]) 
    if(close<=close[1]) 
        bottom:= nz(bottom[1])+(close[1]/close) 
    else 
        bottom:=nz(bottom[1]) 
    if (top+bottom/2>=0) 
        1-1/(1+(top/2)/(bottom/2)) 
abs_partial=absolute_str_formula() 
abs_final = abs_partial - ta.sma(abs_partial,50) 
//macd 
fast_length = input(title="Fast Length", defval=23) 
slow_length = input(title="Slow Length", defval=11) 
src = input(title="Source", defval=open) 
signal_length = input.int(title="Signal Smoothing", minval = 1, maxval = 50, defval = 6) 
sma_source = input.string(title="Oscillator MA Type", defval="EMA", options=["SMA", "EMA"]) 
sma_signal = input.string(title="Signal Line MA Type", defval="SMA", options=["SMA", "EMA"]) 
// Calculating 
fast_ma = sma_source == "SMA" ? ta.sma(src, fast_length) : ta.ema(src, fast_length) 
slow_ma = sma_source == "SMA" ? ta.sma(src, slow_length) : ta.ema(src, slow_length) 
macd = fast_ma - slow_ma 
signal = sma_signal == "SMA" ? ta.sma(macd, signal_length) : ta.ema(macd, signal_length) 
hist = macd - signal 
long= abs_final > 0 and hist <0 and out9<out21 and out21<out50 
short = abs_final <0 and hist >0 and out9>out21 and out21>out50 
long_exit = abs_final <0 and hist >0 and out9>out21 and out21>out50 
short_exit = abs_final > 0 and hist <0 and out9<out21 and out21<out50 
strategy.entry("long", strategy.long, when = long and barstate.isconfirmed, alert_message = message_long_entry) 
strategy.entry("short", strategy.short, when = short and barstate.isconfirmed, alert_message = message_short_entry) 
strategy.close("long", when = long_exit and barstate.isconfirmed, alert_message = message_long_exit) 
strategy.close("short", when = short_exit and barstate.isconfirmed, alert_message = message_short_exit)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-02-28. 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/The-Breakthrough-Callback-Trading-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.