Dual Timeframe Volatility Spread Trading Strategy

The dual timeframe volatility spread trading strategy judges the overbought/oversold status of the market by calculating the spread between RSI indicators of two different time cycles to implement low risk trend trading.

Pine ScriptArbitrage Volatility Breakout RSI EMA SMA

Author: ChaoZhang
Complexity: Intermediate · 62 lines
Parameters: 14
Backtest bar: 2h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Principle

The core indicators of this strategy are shortTermXtrender and longTermXtrender. shortTermXtrender calculates the RSI spread on the short-term timeframe, and longTermXtrender calculates the RSI spread on the long-term timeframe.

The short-term timeframe adopts the price difference between 7-day EMA and 4-day LMA to calculate RSI, and then the price difference with 50 constitutes shortTermXtrender. The long-term timeframe adopts the price difference between RSI of 4-day EMA and 50 to constitute longTermXtrender.

When shortTermXtrender crosses above 0, go long; when longTermXtrender crosses above 0, also go long. The stop loss principle after going long is to stop loss when shortTermXtrender crosses below 0; when longTermXtrender crosses below 0, stop loss too.

In this way, by judging on dual timeframes, more false breakouts can be filtered out.

Advantage Analysis

The biggest advantage of this strategy is that the trend judgment is accurate. The combination of dual timeframes can effectively filter out noise and lock in the target trend direction. This provides a guarantee for low-risk trend tracking trading.

In addition, the strategy provides room for parameter optimization. Users can adjust parameters such as SMA cycle and RSI parameters according to different varieties and time cycles to optimize strategy results.

Risk Analysis

The main risk of this strategy is the wrong judgment of long and short. In oscillating markets, it is easy to generate wrong signals. If the position is still opened at this time, there will be the risk of loss.

In addition, improper parameter settings can also lead to poor results. If the time cycle parameter is set too short, the probability of misjudgment will increase; if the time cycle parameter is set too long, the opportunity for the trend will be missed. This requires users to test and optimize parameters for different markets.

Optimization Directions

The strategy can be optimized in the following aspects:

Increase profit taking mechanism. Currently there is no profit taking setting in the strategy. Profit can be taken in time after reaching the target profit.
Increase position management. Positions can be dynamically adjusted based on capital size, volatility and other indicators.
Test parameter settings for different varieties. Users can test the optimal parameter combination by backtesting different timeframes such as daily and 60 minutes.
Increase machine learning assisted judgment. Models can be trained to determine market conditions and dynamically adjust strategy parameters to improve win rate.

Summary

The dual timeframe volatility spread trading strategy achieves efficient trend capturing by constructing dual timeframe indicators. The strategy has large optimization space. Users can optimize through parameter adjustment, profit taking management, position management, etc. to obtain better strategy results. This strategy is suitable for users with some trading experience.

Parameters

Name	Default	What it controls
v_input_1	7	ShortTermSMA
v_input_2	4	ShortTermLMA
v_input_3	2	ShortTermRSI
v_input_4	4	LongTermMA
v_input_5	2	LongTermRSI
v_input_6	true	UseFactors
v_input_7	true	TradeShortTerm
v_input_8	true	TradeLongTerm
v_input_9	true	From Day
v_input_10	true	From Month
v_input_11	2018	From Year
v_input_12	true	To Day
v_input_13	true	To Month
v_input_14	2020	To Year

Source Pine Script

/*backtest
start: 2024-01-18 00:00:00
end: 2024-02-17 00:00:00
period: 2h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
//study("MavXtrender")
strategy("MavXtrender")

ShortTermSMA = input(7)
ShortTermLMA = input(4)
ShortTermRSI = input(2)

LongTermMA  = input(4)
LongTermRSI  = input(2)

UseFactors = input(true)
TradeShortTerm = input(true)
TradeLongTerm = input(true)

count = TradeShortTerm == true ? 1 : 0
count := TradeLongTerm == true ? count + 1 : count
// set position size
Amount = strategy.equity / (close * count)

ShortTermLMA := UseFactors == true ? round(ShortTermSMA * ShortTermLMA) : ShortTermLMA
ShortTermRSI := UseFactors == true ? round(ShortTermSMA * ShortTermRSI) : ShortTermRSI
LongTermMA := UseFactors == true ? round(ShortTermSMA * LongTermMA) : LongTermMA
LongTermRSI := UseFactors == true ? round(ShortTermSMA * LongTermRSI) : LongTermRSI

shortTermXtrender = rsi(ema(close, ShortTermSMA) - ema(close, ShortTermLMA), ShortTermRSI ) - 50
longTermXtrender  = rsi( ema(close, LongTermMA), LongTermRSI ) - 50

// === INPUT BACKTEST RANGE ===
FromDay   = input(defval = 1, title = "From Day", minval = 1, maxval = 31)
FromMonth = input(defval = 1, title = "From Month", minval = 1, maxval = 12)
FromYear  = input(defval = 2018, title = "From Year", minval = 2012)
ToDay     = input(defval = 1, title = "To Day", minval = 1, maxval = 31)
ToMonth   = input(defval = 1, title = "To Month", minval = 1, maxval = 12)
ToYear    = input(defval = 2020, title = "To Year", minval = 2012)

// === FUNCTION EXAMPLE ===
start     = timestamp(FromYear, FromMonth, FromDay, 00, 00)  // backtest start window
finish    = timestamp(ToYear, ToMonth, ToDay, 23, 59)        // backtest finish window
window()  => true

strategy.entry("ShortTerm", strategy.long, qty = Amount, when = window() and crossover(shortTermXtrender,0) and TradeShortTerm)
strategy.entry("LongTerm", strategy.long, qty = Amount, when = window() and crossover(longTermXtrender,0) and TradeLongTerm)

strategy.close("ShortTerm", when = crossunder(shortTermXtrender,0) or time > finish)
strategy.close("LongTerm", when = crossunder(longTermXtrender,0) or time > finish)

shortXtrenderCol = shortTermXtrender > 0 ? shortTermXtrender > shortTermXtrender[1] ? color.lime : #228B22 : shortTermXtrender > shortTermXtrender[1] ? color.red : #8B0000
plot(shortTermXtrender, color=shortXtrenderCol, style=plot.style_columns, linewidth=1, title="B-Xtrender Osc. - Histogram", transp = 50)

longXtrenderCol = longTermXtrender> 0 ? longTermXtrender > longTermXtrender[1] ? color.lime : #228B22 : longTermXtrender > longTermXtrender[1] ? color.red : #8B0000
plot(longTermXtrender , color=longXtrenderCol, style=plot.style_histogram, linewidth=2, title="B-Xtrender Trend - Histogram", transp = 80)
plot(longTermXtrender , color=color.white,     style=plot.style_line,      linewidth=1, title="B-Xtrender Trend - Line",      transp = 80)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-02-18. 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 spread arbitrage engine with WebSocket order routing and leg-risk controls from strategies/Dual-Timeframe-Volatility-Spread-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.