Dynamic Take Profit Following Trend Strategy

Pine ScriptTrend following Machine learning Breakout RSI EMA SMA Bollinger Bands

Author: ChaoZhang
Complexity: Intermediate · 104 lines
Parameters: 0
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

The Dynamic Take Profit Following Trend strategy detects long-term trends and short-term pullbacks to achieve buying low and selling high, with the goal of chasing uptrends. The strategy also uses volatility units to detect the size of wins and losses so that it can be applied to all coins without worrying about percentage changes.

Strategy Logic

The buying logic of this strategy is: when a long-term uptrend appears (200-day EMA goes up, 200-day RSI greater than 51) and a short-term pullback happens (last 2 candlesticks show decreased closing prices), long positions are opened.

The selling logic is: take profit when price increases more than 1 volatility unit; stop loss when price decreases more than 2 volatility units.

The volatility unit is calculated as: 2 times the standard deviation of closing prices in the last 50 days. This can detect volatility conditions of different coins automatically without needing manual percentage settings.

Advantage Analysis

The biggest advantage of this strategy is that it can dynamically detect volatility sizes of different coins and set stop loss/take profit levels accordingly. This avoids the problem of fixed percentage settings and can automatically adapt to more coins.

Another advantage is combining long-term and short-term judgments can effectively filter out false breakouts. Using the long-term trend to judge potentially uptrending coins and combining it with short-term pullback signals can avoid false signals like Bollinger squeezes.

Risk Analysis

The biggest risk of this strategy is the stop loss/take profit unit settings. If volatility is too high, take profit distances may be too close to keep chasing the uptrend; if volatility is too low, stop loss may trigger too fast. This needs longer period EMAs as an assist to avoid errors in volatility unit judgments.

Another risk is the strategy's reliance on short-term trends. If there is a long-term uptrend without a short-term pullback, the entry timing would be missed. This may need additional assist indicators.

Optimization Directions

The strategy can be optimized in the following directions:

Add longer-period EMA judgments to avoid volatility unit errors
Add indicators like trading volumes to judge trends, reduce reliance on short-term candlesticks
Optimize entry and exit conditions, set stricter entry rules
Combine machine learning algorithms to determine trend direction, achieve higher win rate

Conclusion

The Dynamic Take Profit Following Trend Strategy has clear logic at its core-dynamically setting stop loss/take profit units. This strategy can automatically adapt settings across coins without needing manual percentage inputs. Meanwhile, combining double confirmation of long-term and short-term trends can effectively filter out false signals. With further optimizations, this strategy can become a highly efficient trend chasing strategy.

Source Pine Script

/*backtest
start: 2022-12-22 00:00:00
end: 2023-12-28 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

// @version=4
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © BHD_Trade_Bot

strategy(shorttitle='Take Profit On Trend',
 title='Take Profit On Trend (by BHD_Trade_Bot)',
 overlay=true,
 initial_capital = 15,
 default_qty_type = strategy.cash,
 default_qty_value = 15,
 commission_type=strategy.commission.percent,
 commission_value=0.1)



//Backtest Time
start_day = 1
start_month = 1
start_year = 2021
end_day = 1
end_month = 1
end_year = 2050
start_time = timestamp(start_year, start_month, start_day, 00, 00)
end_time = timestamp(end_year, end_month, end_day, 23, 59)
is_back_test_time() =>
    time >= start_time and time <= end_time ? true : false

// Last bar
h1_last_bar = (timenow - time)/1000/60/60 < 2



// EMA
ema50 = ema(close, 50)
ema200 = ema(close, 200)

// RSI length 200
rsi200 = rsi(close, 200)

// Bollinger Bands length 50
bb50 = 2 * stdev(close, 50)

// BHD Unit
bhd_unit = sma(bb50, 100)
bb50_upper = ema50 + bhd_unit
bb50_lower = ema50 - bhd_unit



// All n candles is going down
all_body_decrease(n) =>
    isValid = true
    for i = 0 to (n - 1)
        if (close[i] > close[i + 1])
            isValid := false
            break
    isValid



// ENTRY

// Long-term uptrend
entry_condition1 = rsi200 > 51 

// Short-term downtrend
entry_condition2 = all_body_decrease(2) 

ENTRY_CONDITION = entry_condition1 and entry_condition2

if (ENTRY_CONDITION and is_back_test_time())
    strategy.entry("entry", strategy.long)



// CLOSE CONDITIONS

// Price increase 1 BHD unit
TAKE_PROFIT = close > strategy.position_avg_price + bhd_unit

// Price decrease 2 BHD unit
STOP_LOSS = close < strategy.position_avg_price - bhd_unit * 2

CLOSE_CONDITION = TAKE_PROFIT or STOP_LOSS

if (CLOSE_CONDITION or h1_last_bar)
    strategy.close("entry")



// Draw
plot(ema50)
plot(ema200, color=color.yellow)
plot(bb50_upper)
plot(bb50_lower)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-12-29. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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