RSI MACD Crossover Double MA Tracking Strategy

Pine ScriptTrend following Mean reversion Volatility RSI MACD SMA

Author: ChaoZhang
Complexity: Intermediate · 89 lines
Parameters: 13
Backtest bar: 2h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy combines RSI indicator, MACD indicator and double moving averages to achieve trend tracking and positioning effects in volatility market. It uses RSI indicator to judge overbought and oversold conditions, MACD to determine entry and exit points with fast and slow MA crossover, and double MAs to filter out some noisy trading opportunities during the trend.

Strategy Logic

Calculate RSI indicator for overbought and oversold

Calculate price change uptrend and downtrend
Compute RSI based on the price change
Determine overbought and oversold levels

Calculate MACD for crossover

Compute fast MA, slow MA and signal line
Enter long on golden cross and exit on death cross
Plot the crossover situations

Implement double MA filter

Compute fast and slow moving averages
Only consider trading when fast MA crosses above slow MA
Filter noise and follow the trend

Combine indicators for entry

Filter entry signal with RSI, MACD and double MA
Improve accuracy and stability of strategy

Advantage Analysis

Combination of multiple indicators improves accuracy
Trend following filters noise and enhances stability
RSI spots potential reversal points
MACD crossover provides simple entry and exit signals
Double MA removes most countertrend trades
Easy to understand with few parameters, good for learning

Risk Analysis

Risk of overfitting with multiple indicators
Double MA sacrifices flexibility and may miss chances
RSI and MACD parameters need careful selection
Pay attention to stop loss based on symbol
Requires periodic re-tuning of parameters

Optimization Directions

Adjust RSI parameters for different symbols
Optimize double MA periods for better tracking
Add stop loss to control single trade loss
Incorporate more indicators to enrich combo
Develop adaptive parameter model for auto-tuning

Summary

This strategy combines RSI, MACD and double MA to identify and track trends, and filters signals through multiple layers. It is very suitable for beginners to learn and improve. The advantage lies in its simplicity and adaptiveness. Fine tuning of parameters can generate decent steady returns. Next steps may include adding more indicators, developing adaptive parameter model to auto optimize for different market environments.

Parameters

Name	Default	What it controls
v_input_1	2017	Backtest Start Year
v_input_2	true	Backtest Start Month
v_input_3	2	Backtest Start Day
v_input_4	2019	Backtest Stop Year
v_input_5	7	Backtest Stop Month
v_input_6	30	Backtest Stop Day
v_input_7	true	Color Background?
v_input_8	14	Length
v_input_9	9	Fast Length
v_input_10	72	Slow Length
v_input_11	9	Signal Length
v_input_12	234	Control MA
v_input_13	true	Buy on crossover control MA?

Source Pine Script

/*backtest
start: 2023-09-22 00:00:00
end: 2023-10-22 00:00:00
period: 2h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=3

// strategy(title="RSI MACD", precision = 6, pyramiding = 1, default_qty_type = strategy.percent_of_equity, default_qty_value = 99, commission_type = strategy.commission.percent, commission_value = 0.25, initial_capital = 1000)

// Component Code Start
// Example usage:
// if testPeriod()
//   strategy.entry("LE", strategy.long)
testStartYear = input(2017, "Backtest Start Year")
testStartMonth = input(01, "Backtest Start Month")
testStartDay = input(2, "Backtest Start Day")
testPeriodStart = timestamp(testStartYear,testStartMonth,testStartDay,0,0)

testStopYear = input(2019, "Backtest Stop Year")
testStopMonth = input(7, "Backtest Stop Month")
testStopDay = input(30, "Backtest Stop Day")
testPeriodStop = timestamp(testStopYear,testStopMonth,testStopDay,0,0)

// A switch to control background coloring of the test period
testPeriodBackground = input(title="Color Background?", type=bool, defval=true)
testPeriodBackgroundColor = testPeriodBackground and (time >= testPeriodStart) and (time <= testPeriodStop) ? #00FF00 : na
bgcolor(testPeriodBackgroundColor, transp=97)

testPeriod() => true
// Component Code Stop

//standard rsi template
src = ohlc4, len = input(14, minval=1, title="Length")
up = rma(max(change(src), 0), len)
down = rma(-min(change(src), 0), len)
rsi = down == 0 ? 100 : up == 0 ? 0 : 100 - (100 / (1 + up / down))
plot(rsi, color=#87ff1a)
band1 = hline(80)
band = hline(50)
band0 = hline(20)
fill(band1, band0, color=purple, transp=90)

//macd

fast_length = input(title="Fast Length",  defval=9)
slow_length = input(title="Slow Length",  defval=72)
signal_length = input(title="Signal Length",  defval=9)

fast_ma = sma(rsi, fast_length) 
slow_ma = sma(rsi, slow_length) 
shortma = sma(ohlc4, fast_length)
longma = sma(ohlc4, slow_length)
controlmainput = input(title = "Control MA", defval = 234)
controlma = sma(ohlc4, controlmainput)
macdx = fast_ma - slow_ma
signalx = sma(macdx, signal_length)
hist = macdx - signalx
ma_hist = shortma - controlma
macd = macdx + 50
signal = signalx + 50

plot(macd,"macd", color = fuchsia)
plot(hist,"hist", style = histogram, color = fuchsia)
//plot(ma_hist,"ma hist", style = histogram, color = orange)
plot(signal,"signal", color = white)

//input
control_buy_toggle = input(true, "Buy on crossover control MA?", type = bool)
buy_on_control = control_buy_toggle == true? true : false

//conditions
buy = buy_on_control == true? ma_hist > 0 and shortma > longma and crossover(macd,signal) or crossover(shortma, controlma) : ma_hist > 0 and shortma > longma and crossover(macd,signal)
sell = ma_hist > 0 and shortma > longma and crossunder(macd,signal)
stop = crossunder(shortma, longma) or crossunder(shortma, controlma)

plotshape(buy,"buy", shape.triangleup, location.bottom, green, size = size.tiny)
plotshape(sell,"sell", shape.triangledown, location.bottom, red, size = size.tiny)
plotshape(stop,"stop",shape.circle,location.bottom, white, size = size.tiny)

if testPeriod()
    strategy.entry("buy", true, when = buy, limit = close)
    strategy.close("buy", when = sell)
    strategy.close("buy", when = stop)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-10-23. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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