Multi Timeframe EMA Trend Strategy with Daily High Low Breakout System

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

Author: ChaoZhang
Complexity: Starter · 51 lines
Parameters: 0
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This is a quantitative trading strategy that combines daily high-low breakouts with multi-timeframe EMA trends. The strategy primarily identifies trading opportunities by monitoring price breakouts of the previous day's high and low levels, combined with EMA trends and the Chaikin Money Flow (CMF) indicator. It utilizes 200-period EMAs on both hourly and daily timeframes to enhance trading accuracy through multiple technical indicator validation.

Strategy Principles

The core logic includes the following key elements:

Uses request.security function to obtain previous day's high and low prices as key support and resistance levels.
Incorporates 24-period EMA as the baseline for trend determination.
Implements CMF (20-period) as a comprehensive indicator of volume and price to assess market money flow.
Calculates 200 EMAs on both current and 1-hour timeframes to determine larger trend directions.

Specific trading rules: Long Entry: Price breaks above previous day's high + Close above EMA + Positive CMF Short Entry: Price breaks below previous day's low + Close below EMA + Negative CMF Exit: Cross below EMA for longs, cross above EMA for shorts

Strategy Advantages

Multiple technical indicator validation improves trading reliability
Multi-timeframe analysis provides comprehensive trend assessment
CMF indicator integration better captures market money flow conditions
Previous day's high-low levels align with market participants' trading habits
Clear strategy logic that's easy to understand and execute
Well-defined entry and exit conditions minimize subjective judgment

Strategy Risks

May generate frequent false signals in ranging markets
Not sufficiently responsive to instantaneous price breakouts
Potential missed opportunities at key levels
Lacks consideration of larger timeframe trends
May experience significant drawdowns during extreme market volatility

Risk Control Suggestions:

Implement appropriate stop-loss levels
Adjust parameters based on market conditions
Add trend filters
Consider incorporating volatility indicators

Optimization Directions

Implement adaptive parameter optimization mechanisms
Add more market condition filters
Optimize stop-loss and take-profit mechanisms
Include volatility indicators for different market conditions
Consider position management mechanisms
Add volume analysis indicators

Summary

This is a complete trading system combining multiple technical indicators and multi-timeframe analysis. The strategy seeks trading opportunities through comprehensive analysis of intraday high-low breakouts, moving average trends, and money flow. While certain risks exist, the strategy holds good practical value through proper risk control and continuous optimization. Traders are advised to conduct thorough backtesting and parameter optimization before live implementation.

Source Pine Script

/*backtest
start: 2024-10-28 00:00:00
end: 2024-11-27 00:00:00
period: 1h
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy(title='The security Daily HIGH/LOW strategy', overlay=true, initial_capital=10000, calc_on_every_tick=true, 
         default_qty_type=strategy.percent_of_equity, default_qty_value=100, 
         commission_type=strategy.commission.percent, commission_value=0.1)

// General Inputs
len = input.int(24, minval=1, title='Length MA', group='Optimization parameters')
src = input.source(close, title='Source MA', group='Optimization parameters')
out = ta.ema(src, len)

length = input.int(20, minval=1, title='CMF Length', group='Optimization parameters')
ad = close == high and close == low or high == low ? 0 : (2 * close - low - high) / (high - low) * volume
mf = math.sum(ad, length) / math.sum(volume, length)

// Function to get daily high and low
f_secureSecurity(_symbol, _res, _src) =>
    request.security(_symbol, _res, _src[1], lookahead=barmerge.lookahead_on)

pricehigh = f_secureSecurity(syminfo.tickerid, 'D', high)
pricelow = f_secureSecurity(syminfo.tickerid, 'D', low)

// Plotting previous daily high and low
plot(pricehigh, title='Previous Daily High', style=plot.style_linebr, linewidth=2, color=color.new(color.white, 0))
plot(pricelow, title='Previous Daily Low', style=plot.style_linebr, linewidth=2, color=color.new(color.white, 0))

// Entry Conditions
short = ta.crossunder(low, pricelow) and close < out and mf < 0
long = ta.crossover(high, pricehigh) and close > out and mf > 0

if short and barstate.isconfirmed
    strategy.entry('short', strategy.short, stop=pricelow[1])
    strategy.close('short', when=close > out)

if long and barstate.isconfirmed
    strategy.entry('long', strategy.long, stop=pricehigh[1])
    strategy.close('long', when=close < out)

// 200 EMA on 1-hour timeframe
ema_200 = ta.ema(close, 200)
ema_200_1h = request.security(syminfo.tickerid, "60", ta.ema(close, 200))

plot(ema_200_1h, color=color.purple, title="200 EMA (1H)")
plot(ema_200, color=color.white, title="200 EMA")

Published by ChaoZhang on the FMZ strategy square · last modified 2024-11-28. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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