EMA Multi DCA Strategy with Trailing Stop Loss and Profit Target

Pine ScriptDCA & fixed investment Machine learning Trend following EMA ATR

Author: ChaoZhang
Complexity: Starter · 82 lines
Parameters: 4
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

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Overview

This strategy utilizes dynamic multiple EMAs as entry signals combined with trailing stop loss and profit target mechanisms for risk management. It takes advantage of the smoothing nature of EMAs to identify trends and control cost via multi-DCA entries. In addition, the integration of adaptive stop loss and profit taking features enhances the automation process.

Strategy Logic

Indicators

EMA5, EMA10, EMA20, EMA50, EMA100, EMA 200
Average True Range (ATR)

Entry Signals

Triggers long entry when price crosses or moves inside a range of selected EMA periods. Typical EMAs include 5, 10, 20, 50, 100, 200 periods. This strategy uses 1% range of EMA as the entry criteria.

Risk Management

Incorporates multiple risk control mechanisms:

ATR Stop Loss: Close all positions when ATR exceeds threshold
Entry Frequency Limit: Control maximum number of entries
Trailing Stop Loss: Dynamic stop loss based on price movement

Profit Taking

Set profit target price levels for exits

Advantages

Identify trends using EMAs with noise filtering
Cost averaging via multi-DCA entries
Enhanced entry signals using EMA combos
Adaptive stop loss mechanism
Take profit control for profit protection

Risks & Improvements

EMAs tuning needs optimization for different markets
Excessive DCA entries may occupy too much capital
Stop loss percentage needs backtesting

Enhancement Strategies

Utilize advanced EMA systems for better trend identification
Multi-variable optimization of DCA frequency and stop loss percentage
Incorporate machine learning models for price change forecasts
Integrate position sizing module to manage overall capital utilization

Conclusion

The strategy encompasses EMA trend detection, multi-DCA cost averaging, trailing stop loss, target profit taking and more. There remains ample potential in tuning parameters and enhancing risk controls. Overall, this highly adaptive and versatile strategy offers investors stable alpha generation capabilities.

Parameters

Name	Default	What it controls
v_input_1	10	ATR Sell Threshold
v_input_2	3000	Buy Limit
v_input_3	true	Trailing Stop Percentage
v_input_4	3	Profit Target Percentage

Source Pine Script

/*backtest
start: 2023-01-12 00:00:00
end: 2024-01-18 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
strategy("EMA DCA Strategy with Trailing Stop and Profit Target", overlay=true )

// Define the investment amount for when the condition is met
investment_per_condition = 6

// Define the EMAs
ema5 = ema(close, 5)
ema10 = ema(close, 10)
ema20 = ema(close, 20)
ema50 = ema(close, 50)
ema100 = ema(close, 100)
ema200 = ema(close, 200)

// Define ATR sell threshold
atr_sell_threshold = input(title="ATR Sell Threshold", type=input.integer, defval=10, minval=1)

// Helper function to find if the price is within 1% of the EMA
isWithin1Percent(price, ema) =>
    ema_min = ema * 0.99
    ema_max = ema * 1.01
    price >= ema_min and price <= ema_max

// Control the number of buys
var int buy_count = 0
buy_limit = input(title="Buy Limit", type=input.integer, defval=3000)

// Calculate trailing stop and profit target levels
trail_percent = input(title="Trailing Stop Percentage", type=input.integer, defval=1, minval=0, maxval=10)
profit_target_percent = input(title="Profit Target Percentage", type=input.integer, defval=3, minval=1, maxval=10)

// Determine if the conditions are met and execute the strategy
checkConditionAndBuy(emaValue, emaName) =>
    var int local_buy_count = 0 // Create a local mutable variable
    if isWithin1Percent(close, emaValue) and local_buy_count < buy_limit
        strategy.entry("Buy at " + emaName, strategy.long, qty=investment_per_condition / close, alert_message ="Buy condition met for " + emaName)
        local_buy_count := local_buy_count + 1
        // alert("Buy Condition", "Buy condition met for ", freq_once_per_bar_close)
        
    local_buy_count // Return the updated local_buy_count

// Add ATR sell condition
atr_condition = atr(20) > atr_sell_threshold
if atr_condition
    strategy.close_all()
    buy_count := 0 // Reset the global buy_count when selling

// Strategy execution
buy_count := checkConditionAndBuy(ema5, "EMA5")
buy_count := checkConditionAndBuy(ema10, "EMA10")
buy_count := checkConditionAndBuy(ema20, "EMA20")
buy_count := checkConditionAndBuy(ema50, "EMA50")
buy_count := checkConditionAndBuy(ema100, "EMA100")
buy_count := checkConditionAndBuy(ema200, "EMA200")

// Calculate trailing stop level
trail_offset = close * trail_percent / 100
trail_level = close - trail_offset

// Set profit target level
profit_target_level = close * (1 + profit_target_percent / 100)

// Exit strategy: Trailing Stop and Profit Target
strategy.exit("TrailingStop", from_entry="Buy at EMA", trail_offset=trail_offset, trail_price=trail_level)
strategy.exit("ProfitTarget", from_entry="Buy at EMA",  when=close >= profit_target_level)

// Plot EMAs
plot(ema5, title="EMA 5", color=color.red)
plot(ema10, title="EMA 10", color=color.orange)
plot(ema20, title="EMA 20", color=color.yellow)
plot(ema50, title="EMA 50", color=color.green)
plot(ema100, title="EMA 100", color=color.blue)
plot(ema200, title="EMA 200", color=color.purple)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-19. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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