Dynamic Multi Timeframe Pivot Trend Breakthrough Strategy

Pine ScriptBreakout Trend following Multi-timeframe RSI EMA Pivots / S&R

Author: ianzeng123
Complexity: Intermediate · 112 lines
Parameters: 0
Backtest bar: 1d · swing
Tested on: ETH/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy is a dynamic trend breakthrough trading approach based on multi-timeframe pivots and the Relative Strength Index (RSI). By combining weekly-level price support and resistance levels with the RSI indicator, the strategy aims to capture trend opportunities in financial markets while providing refined position management and risk control mechanisms.

Strategy Principles

The core principles of the strategy include the following key steps:

Multi-Timeframe Price Pivot Calculation:

Calculate key support and resistance levels using weekly-level previous candle's close, high, and low prices
Compute typical support levels (S1, S2, S3) and resistance levels (R1, R2, R3)
Dynamically adjust support and resistance levels' sensitivity through a factor

RSI Indicator Dynamic Optimization:

Calculate RSI with a 21-period length
Introduce Exponential Moving Average (EMA) to smooth RSI
Construct a composite indicator combining raw RSI and EMA smoothed values

Trading Signal Generation:

Long Entry: Composite indicator crosses above 0
Long Exit: Highest price breaks R3 resistance level
Short Entry: Lowest price breaks S3 support level
Short Exit: Composite indicator crosses below 0

Strategy Advantages

Multi-Timeframe Perspective: Effectively filter short-term market noise by introducing weekly-level data
Flexible Position Management: Staged take-profit mechanism reduces single-trade risk
Dynamic Indicator Construction: Combine RSI and EMA to improve signal accuracy
Symmetric Long and Short Trading Logic: Provide flexible strategies for different market environments
Controllable Risk: Built-in stop-loss and staged take-profit mechanisms

Strategy Risks

Indicator Lag: RSI and price pivots may have latency issues
Parameter Sensitivity: Strategy performance highly depends on parameter selection
Transaction Cost Impact: Frequent trading may lead to high commission fees
Extreme Market Conditions: Trend reversal and violent fluctuations may cause strategy failure

Strategy Optimization Directions

Introduce machine learning algorithms to optimize parameter selection
Add volume and volatility filtering mechanisms
Combine more technical indicators for signal verification
Develop dynamic stop-loss and take-profit algorithms
Introduce more complex position sizing management models

Summary

The strategy builds a relatively robust trend breakthrough trading method through multi-timeframe and multi-indicator comprehensive analysis. Its core advantage lies in dynamically capturing market trends and fine-grained risk management. Future optimization spaces include algorithmic intelligence and iterative risk control model development.

Source Pine Script

/*backtest
start: 2024-03-31 00:00:00
end: 2025-03-29 08:00:00
period: 1d
basePeriod: 1d
exchanges: [{"eid":"Futures_Binance","currency":"ETH_USDT"}]
*/

// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © yuxishejiang

//@version=6
//@version=5
strategy(title="BTCMidline strategy optimization-V2", overlay=true, pyramiding=1, initial_capital=5000, default_qty_type=strategy.percent_of_equity, default_qty_value=100, calc_on_order_fills=false, slippage=0, commission_type=strategy.commission.percent, commission_value=0.075)

// Core Parameters
strat_dir_input = input.string(title="Strategy Direction", defval="long", options=["long", "short"])
strat_dir_value = strat_dir_input == "long" ? strategy.direction.long : strat_dir_input == "short" ? strategy.direction.short : strategy.direction.all
strategy.risk.allow_entry_in(strat_dir_value)

// Indicator calculation
higherTF = input.timeframe("W", "Higher Timeframe")
pc = request.security(syminfo.tickerid, higherTF, close[1], barmerge.gaps_off, barmerge.lookahead_on)
ph = request.security(syminfo.tickerid, higherTF, high[1], barmerge.gaps_off, barmerge.lookahead_on)
pl = request.security(syminfo.tickerid, higherTF, low[1], barmerge.gaps_off, barmerge.lookahead_on)

PP = (ph + pl + pc) / 3
R1 = PP + (PP - pl)
S1 = PP - (ph - PP)
R2 = PP + (ph - pl)
S2 = PP - (ph - pl)
factor = input.int(2, "Factor")
R3 = ph + factor * (PP - pl)
S3 = pl - 2 * (ph - PP)

length = input.int(21, "RSI Length")
p = close
vrsi = ta.rsi(p, length)
pp_ema = ta.ema(vrsi, length)
d = (vrsi - pp_ema) * 5
cc = (vrsi + d + pp_ema) / 2

// Position management variables
var float entry_qty = na

// Trade execution logic
longEntry = ta.crossover(cc, 0)
longExit = ta.crossover(high, R3)  // Use real-time highest price judgment

shortEntry = ta.crossunder(low, S3)  // Change to UseS3support level
shortExit = ta.crossunder(cc, 0)     // Synchronously modify to cross below

if (longEntry)
    strategy.entry("Long", strategy.long)
    entry_qty := strategy.position_size

if (strategy.position_size > 0 and longExit)
    strategy.close("Long", comment="5MDivergence Exit")

if (shortEntry)
    strategy.entry("Short", strategy.short)
    entry_qty := strategy.position_size

if (strategy.position_size < 0 and shortExit)
    strategy.close("Short", comment="Short Exit")

// Take profit and stop loss module
per(pcnt) =>
    strategy.position_size != 0 ? math.round(math.abs(pcnt/100 * strategy.position_avg_price / syminfo.mintick)) : na

stoploss = input.float(15, "Stop Loss (%)", minval=0.01)
tp1 = input.float(3, "Take Profit 1 (%)", minval=0.01)
tp2 = input.float(5, "Take Profit 2 (%)", minval=0.01)
tp3 = input.float(7, "Take Profit 3 (%)", minval=0.01)
tp4 = input.float(10, "Take Profit 4 (%)", minval=0.01)

// Staged position closing logic
if strategy.position_size != 0
    qty_total = math.abs(entry_qty)
    qty1 = math.floor(qty_total * 0.25)
    qty2 = math.floor(qty_total * 0.25)
    qty3 = math.floor(qty_total * 0.25)
    qty4 = qty_total - (qty1 + qty2 + qty3)
    
    if strategy.position_size > 0
        strategy.exit("x1", qty=qty1, profit=per(tp1), loss=per(stoploss))
        strategy.exit("x2", qty=qty2, profit=per(tp2), loss=per(stoploss))
        strategy.exit("x3", qty=qty3, profit=per(tp3), loss=per(stoploss))
        strategy.exit("x4", qty=qty4, profit=per(tp4), loss=per(stoploss))
    else
        strategy.exit("x1", qty=qty1, profit=per(tp1), loss=per(stoploss))
        strategy.exit("x2", qty=qty2, profit=per(tp2), loss=per(stoploss))
        strategy.exit("x3", qty=qty3, profit=per(tp3), loss=per(stoploss))
        strategy.exit("x4", qty=qty4, profit=per(tp4), loss=per(stoploss))

// The visualization part remains unchanged
// Long entry visualization
if (longEntry)
    label.new(bar_index, low, "Long Entry", color=color.green, textcolor=color.white, style=label.style_label_up, size=size.small)

// Long exit visualization
if (strategy.position_size > 0 and longExit)
    label.new(bar_index, high, "Long Exit", color=color.red, textcolor=color.white, style=label.style_label_down, size=size.small)

// Short entry visualization
if (shortEntry)
    label.new(bar_index, high, "Short Entry", color=color.red, textcolor=color.white, style=label.style_label_down, size=size.small)

// Short exit visualization
if (strategy.position_size < 0 and shortExit)
    label.new(bar_index, low, "Short Exit", color=color.green, textcolor=color.white, style=label.style_label_up, size=size.small)

Published by ianzeng123 on the FMZ strategy square · last modified 2025-03-31. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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