Trend Following EMA and RSI Strategy

This strategy takes full advantage of moving averages and relative strength index to identify and follow trends. It only needs two indicators to determine the trend and find proper entry/exit timing. The strategy…

Pine ScriptTrend following Breakout Tools & templates RSI EMA Bollinger Bands ATR

Author: ChaoZhang
Complexity: Intermediate · 70 lines
Parameters: 9
Backtest bar: 2h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

The strategy uses three EMAs with different periods, with EMA-A having the shortest period, EMA-B medium, and EMA-C the longest. When the shorter EMA-A crosses above the longer EMA-B, it signals an upward trend, thus going long. Conversely, when EMA-A crosses below EMA-B, it signals a downward trend, thus going short. To filter false signals, it also uses the longest EMA-C - only considering entry after the price breaks EMA-C.

The strategy also uses RSI to locate exit points. When long, it closes the position if RSI crosses above 70. When short, it exits if RSI falls below 30. This locks in trend profits and prevents losses from expanding further.

Advantage Analysis

Utilizes EMA's strength in trend identification
RSI assists in entry and exit timing
Simple strategy with only 2 indicators
Customizable parameters to adjust strategy style
Profits from early, mid and late trend stages

Risk Analysis

Pullbacks in major trends may generate false signals
Vulnerable to whipsaws in ranging markets
Improper RSI parameters may cause premature exits
EMA periods need careful selection, too short may be noise sensitive, too long may miss trends

These risks can be reduced by optimizing RSI parameters, adding filters, and combining with trend analysis.

Optimization Directions

Optimize RSI parameters for better profit taking and risk control
Test different EMA period combinations
Add volume or other confirmation indicators
Use ATR for stop loss sizing
Consider reducing position size mid-trend
Optimize entry timing with breakouts, volume, etc
Explore re-entry mechanisms

Summary

This strategy combines trend following and oscillator indicators for trend identification and capturing. With simple parameter and logic optimization, it can be greatly improved while keeping simplicity. It is a very practical trend following template suitable for medium-to-long term investors.

Parameters

Name	Default	What it controls
v_input_int_5	24	# bars
v_input_int_1	14	(?RSI)Length
v_input_source_1_close	0	Source: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_int_2	10	(?Moving Averages)EMA A Length
v_input_int_3	20	EMA B Length
v_input_int_4	100	EMA C Length
v_input_bool_1	true	(?Strategy)Long entries
v_input_bool_2	false	Short entries
v_input_bool_3	true	Close after X # bars

Source Pine Script

/*backtest
start: 2023-08-26 00:00:00
end: 2023-09-25 00:00:00
period: 2h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
//@author Alorse

//@version=5
// strategy(title='Tendency EMA + RSI [Alorse]', shorttitle='Tendece EMA + RSI [Alorse]', overlay=true, pyramiding=0, currency=currency.USD, default_qty_type=strategy.percent_of_equity, initial_capital=1000, default_qty_value=20, commission_type=strategy.commission.percent, commission_value=0.01)

// Bollinger Bands
len = input.int(14, minval=1, title='Length', group='RSI')
src = input.source(close, 'Source', group='RSI')
rsi = ta.rsi(src, len)

// Moving Averages
len_a = input.int(10, minval=1, title='EMA A Length', group='Moving Averages')
out_a = ta.ema(close, len_a)
plot(out_a, title='EMA A', color=color.purple)

len_b = input.int(20, minval=1, title='EMA B Length', group='Moving Averages')
out_b = ta.ema(close, len_b)
plot(out_b, title='EMA B', color=color.orange)

len_c = input.int(100, minval=1, title='EMA C Length', group='Moving Averages')
out_c = ta.ema(close, len_c)
plot(out_c, title='EMA B', color=color.green)

// Strategy Conditions
stratGroup = 'Strategy'
showLong = input.bool(true, title='Long entries', group=stratGroup)
showShort = input.bool(false, title='Short entries', group=stratGroup)
closeAfterXBars = input.bool(true, title='Close after X # bars', tooltip='If trade is in profit', group=stratGroup)
xBars = input.int(24, title='# bars')

entryLong = ta.crossover(out_a, out_b) and out_a > out_c and close > open
exitLong = rsi > 70

entryShort = ta.crossunder(out_a, out_b) and out_a < out_c and close < open
exitShort = rsi < 30


bought = strategy.opentrades[0] == 1 and strategy.position_size[0] > strategy.position_size[1]
entry_price = ta.valuewhen(bought, open, 0)
var int nPastBars = 0
if strategy.position_size > 0
    nPastBars := nPastBars + 1
    nPastBars
if strategy.position_size == 0
    nPastBars := 0
    nPastBars
if closeAfterXBars
    exitLong := nPastBars >= xBars and close > entry_price ? true : exitLong
    exitLong
    exitShort := nPastBars >= xBars and close < entry_price ? true : exitShort
    exitShort

// Long Entry
strategy.entry('Long', strategy.long, when=entryLong and showLong)
strategy.close('Long', when=exitLong)

// Short Entry
strategy.entry('Short', strategy.short, when=entryShort and showShort)
strategy.close('Short', when=exitShort)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-09-26. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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