Grid Trading Strategy Based on Moving Average System

This strategy uses moving average theory to build a grid trading system by judging the market trend through multiple sets of JMA moving averages with different parameters. It aims to capture profits during long-term…

Pine ScriptGrid Breakout Mean reversion

Author: ChaoZhang
Complexity: Intermediate · 94 lines
Parameters: 10
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

Use a combination of 1-20 period JMA moving averages to determine the market trend. When the short period MA is above the long period MA, it is judged as an upward trend, and vice versa as a downward trend.
Open grid trades at trend reversal points, when the short MA crosses below or above the long MA. Establish short positions gradually during uptrends, and long positions during downtrends.
An option to filter based on candlestick color - only buy on red candles and sell on green candles, otherwise disregard color and trade at trend reversal only.
Exits are either tracking stop loss or time-based exit when strategy duration ends.

Advantage Analysis

Using MA system to determine trends can effectively identify long-term trend reversals.
Grid trading can capture profits from range-bound markets without clear trends, with stop loss to control risks.
Customizable JMA parameters, can optimize for different periods, high flexibility.
Candle filter avoids being misled by false breakouts.

Risk Analysis

High whip saw markets without clear trends have higher stop loss risks.
Judgment errors from MA system may lead to incorrect trade signals.
Candle filter risks missing some trading opportunities.
If grid spacing is too wide, insufficient profits; too narrow may result in too many positions and high costs.

Optimization Directions

Test more parameter combinations to find optimal JMA MA combinations for different products.
Incorporate other filters like BOLL bands, KD etc to improve signal quality.
Optimize grid configurations like grid spacing, entry lots etc.
Consider more stop loss methods like gap based, trailing stops etc.

Conclusion

This strategy judges reversals using JMA theory and opens grid trades at turning points to capture profits from long-term trend shifts. Performance can be further improved through parameter optimization. Overall it is suitable for medium-long term holdings to gradually track and profit from trending moves.

Parameters

Name	Default	What it controls
v_input_1	true	Long
v_input_2	true	Short
v_input_3	100	Lot
v_input_4	false	Use Color-filter
v_input_5	1900	From Year
v_input_6	2100	To Year
v_input_7	true	From Month
v_input_8	12	To Month
v_input_9	true	From day
v_input_10	31	To day

Source Pine Script

/*backtest
start: 2022-12-27 00:00:00
end: 2024-01-02 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//Noro
//2019

//@version=3
strategy(title = "Noro's Fishnet Strategy", shorttitle = "Fishnet str", overlay = true, default_qty_type = strategy.percent_of_equity, default_qty_value = 100, pyramiding = 0)

//Settings
needlong = input(true, defval = true, title = "Long")
needshort = input(true, defval = true, title = "Short")
capital = input(100, defval = 100, minval = 1, maxval = 10000, title = "Lot")
usecf = input(false, defval = false, title = "Use Color-filter")
fromyear = input(1900, defval = 1900, minval = 1900, maxval = 2100, title = "From Year")
toyear = input(2100, defval = 2100, minval = 1900, maxval = 2100, title = "To Year")
frommonth = input(01, defval = 01, minval = 01, maxval = 12, title = "From Month")
tomonth = input(12, defval = 12, minval = 01, maxval = 12, title = "To Month")
fromday = input(01, defval = 01, minval = 01, maxval = 31, title = "From day")
today = input(31, defval = 31, minval = 01, maxval = 31, title = "To day")

//JMA
jmax(src, len) =>
    beta = 0.45*(len-1)/(0.45*(len-1)+2)
    alpha = pow(beta, 3)
    L0=0.0, L1=0.0, L2=0.0, L3=0.0, L4=0.0
    L0 := (1-alpha)*src + alpha*nz(L0[1])
    L1 := (src - L0[0])*(1-beta) + beta*nz(L1[1])
    L2 := L0[0] + L1[0]
    L3 := (L2[0] - nz(L4[1]))*((1-alpha)*(1-alpha)) + (alpha*alpha)*nz(L3[1])
    L4 := nz(L4[1]) + L3[0]
	L4

ma01 = jmax(close, 10)
ma02 = jmax(close, 20)
ma03 = jmax(close, 30)
ma04 = jmax(close, 40)
ma05 = jmax(close, 50)
ma06 = jmax(close, 60)
ma07 = jmax(close, 70)
ma08 = jmax(close, 80)
ma09 = jmax(close, 90)
ma10 = jmax(close, 100)
ma11 = jmax(close, 110)
ma12 = jmax(close, 120)
ma13 = jmax(close, 130)
ma14 = jmax(close, 140)
ma15 = jmax(close, 150)
ma16 = jmax(close, 160)
ma17 = jmax(close, 170)
ma18 = jmax(close, 180)
ma19 = jmax(close, 190)
ma20 = jmax(close, 200)

trend = 0
trend := ma01 > ma20 ? 1 : ma01 < ma20 ? -1 : trend[1]
col = trend == 1 ? #00FF7F : #DC143C

plot(ma01, transp = 0, color = col)
plot(ma02, transp = 0, color = col)
plot(ma03, transp = 0, color = col)
plot(ma04, transp = 0, color = col)
plot(ma05, transp = 0, color = col)
plot(ma06, transp = 0, color = col)
plot(ma07, transp = 0, color = col)
plot(ma08, transp = 0, color = col)
plot(ma09, transp = 0, color = col)
plot(ma10, transp = 0, color = col)
plot(ma11, transp = 0, color = col)
plot(ma12, transp = 0, color = col)
plot(ma13, transp = 0, color = col)
plot(ma14, transp = 0, color = col)
plot(ma15, transp = 0, color = col)
plot(ma16, transp = 0, color = col)
plot(ma17, transp = 0, color = col)
plot(ma18, transp = 0, color = col)
plot(ma19, transp = 0, color = col)
plot(ma20, transp = 0, color = col)

//Trading
lot = 0.0
lot := strategy.equity / close * capital / 100

if trend == 1 and (close < open or usecf == false)
    strategy.entry("Long", strategy.long, needlong ? lot : na)

if trend == -1 and (close > open or usecf == false)
    strategy.entry("Short", strategy.short, needshort ? lot : na)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-03. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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