Multi Target EMA Crossover Trend Following Strategy

Pine ScriptTrend following Volatility EMA ATR Volume

Author: ChaoZhang
Complexity: Starter · 67 lines
Parameters: 0
Backtest bar: 2d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy is a trend following system based on EMA crossover signals. It uses a 34-period EMA as the main trend indicator, combined with multiple take-profit levels and risk management mechanisms for fully automated trading. The core concept is to capture trend initiation points through price-EMA crossovers and maximize profit opportunities through multiple profit targets.

Strategy Principles

The strategy operates based on these core principles:

Uses 34-period EMA as trend indicator
Opens long positions at EMA price when price crosses above EMA
Implements triple take-profit targets (5%, 10%, 15%) for staged profit-taking
Sets 7% stop-loss for risk control
Maintains 10% position for long-term trend capture
Implements 8-hour minimum trade interval to prevent overtrading
Supports both fixed volume and dynamic position sizing

Strategy Advantages

Multiple profit targets design performs well in various market conditions
Partial position retention for long-term trends captures extended movements
Supports leveraged trading with adjustable risk exposure
Includes mechanisms to prevent overtrading
Flexible position management with fixed or dynamic sizing options
Fully automated execution requiring no manual intervention
Highly parametric design accommodating different trading styles

Strategy Risks

EMA as a lagging indicator may delay entry timing
Multiple stop-losses possible in ranging markets
Leverage may amplify losses
Fixed percentage stop-loss may lack flexibility in volatile markets
Multiple profit targets might exit strong trends too early Mitigation measures:

Recommended for use in clear trending markets
Adjust stop-loss based on market volatility
Exercise caution with leverage
Regular parameter optimization through backtesting

Optimization Directions

Add trend strength filter to improve entry quality
Implement dynamic stop-loss mechanism using ATR
Incorporate volume confirmation indicators
Develop adaptive profit target mechanism
Add market condition assessment module
Optimize dynamic trade interval adjustment These optimizations can enhance strategy stability and profitability while reducing false signals.

Summary

This is a well-designed trend following strategy with clear logic. It captures trends through EMA crossovers, manages risk with multiple profit targets, and retains partial positions for extended trends. The strategy's high adaptability suits traders with different risk preferences. While inherent risks exist, proper parameter settings and risk management can achieve stable returns.

Source Pine Script

/*backtest
start: 2024-02-08 00:00:00
end: 2025-02-06 08:00:00
period: 2d
basePeriod: 2d
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy("EMA25 Long Strategy", overlay=true)

// Inputs
initial_capital = input.float(50000, title="Initial Capital ($)")
leverage = input.int(1, title="Leverage")
mode = input.string("Fixed Volume", title="Position Sizing Mode", options=["Fixed Volume", "Current Balance"])
ema_length = input.int(34, title="EMA Length")
stop_loss_percent = input.float(7, title="Stop Loss (%)", step=0.1) / 100
take_profit_1_percent = input.float(5, title="Take Profit 1 (%)", step=0.1) / 100
take_profit_2_percent = input.float(10, title="Take Profit 2 (%)", step=0.1) / 100
take_profit_3_percent = input.float(15, title="Take Profit 3 (%)", step=0.1) / 100
position_size_percent = input.float(100, title="Position Size (%)", step=1) / 100
long_term_hold_percent = input.float(10, title="Long Term Hold (%)", step=1) / 100
trade_delay = input.int(8, title="Trade Delay (hours)", minval=1) * 60  // Convert hours to minutes

// Calculate EMA
ema = ta.ema(close, ema_length)
// Plot EMA
plot(ema, title="EMA25", color=color.blue)

// Determine if a new trade can be placed
var float last_trade_time = na
can_trade = na(last_trade_time) or (time - last_trade_time) > trade_delay * 60 * 1000

// Determine position size based on selected mode
var float position_size = na
if (mode == "Fixed Volume")
    position_size := initial_capital * leverage * position_size_percent / close
else
    position_size := strategy.equity * leverage * position_size_percent / close

// Entry Condition
var float entry_price = na
price_crossed_ema_up = ta.crossover(close, ema)
price_crossed_ema_down = ta.crossunder(close, ema)

if ((price_crossed_ema_up or price_crossed_ema_down) and can_trade)
    entry_price := ema
    strategy.entry("Long", strategy.long, qty=position_size, limit=entry_price)
    last_trade_time := time
    label.new(bar_index, entry_price, text="Entry", color=color.green, style=label.style_label_up, textcolor=color.white, size=size.small)

// Stop Loss
strategy.exit("Stop Loss", from_entry="Long", stop=entry_price * (1 - stop_loss_percent))

// Take Profits
take_profit_1_price = entry_price * (1 + take_profit_1_percent)
take_profit_2_price = entry_price * (1 + take_profit_2_percent)
take_profit_3_price = entry_price * (1 + take_profit_3_percent)

strategy.exit("Take Profit 1", from_entry="Long", limit=take_profit_1_price, qty=position_size / 3)
strategy.exit("Take Profit 2", from_entry="Long", limit=take_profit_2_price, qty=position_size / 3)
strategy.exit("Take Profit 3", from_entry="Long", limit=take_profit_3_price, qty=position_size / 3)

// Long Term Hold (10% of position)
hold_qty = position_size * long_term_hold_percent
if (strategy.position_size > hold_qty)
    strategy.close("Long Term Hold")

Published by ChaoZhang on the FMZ strategy square · last modified 2025-02-08. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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