Short term Trading Strategy Based on Trend Following

This strategy identifies strong trends and favorable timing for short-term trading with loss control. It tracks price breakouts of simple moving averages as trend signals and sets stop loss/take profit based on RSI…

Pine ScriptTrend following Machine learning Breakout RSI SMA Stochastic

Author: ChaoZhang
Complexity: Intermediate · 59 lines
Parameters: 12
Backtest bar: 3h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

Calculating multi-period simple moving averages
- Setting 9-day, 50-day and 100-day SMAs
- Short SMA crossing long SMA indicates trend direction
Judging overbought/oversold levels using RSI
- RSI length is 14 periods
- RSI above 70 is overbought, below 30 is oversold
Entering trades when price breaks 9-day SMA
- Go long when price breaks above 9-day SMA
- Go short when price breaks below 9-day SMA
Setting stop loss/take profit based on RSI divergences
- RSI divergence for stop loss
- Take profit when RSI reaches preset levels

Advantage Analysis

Captures short-term trends, suitable for high frequency trading
SMA combos filter trend signals, avoiding bad trades
RSI helps determine timing, effectively control risks
Flexible stop loss/take profit locks short-term profits
Combining indicators improves stability

Risk Analysis

Inaccurate short-term trend judgment causes chasing
False RSI signals expand losses
Improper stop loss/take profit settings reduce profit or magnify losses
High trading frequency increases costs and slippage
Ineffective parameters and abnormal markets impact strategy
Optimize parameters, strict stop loss, manage costs

Optimization Directions

Test different SMA combos to improve trend judgment
Consider additional indicators like STOCH to verify RSI signals
Employ machine learning to determine valid breakouts
Adjust parameters for different products and sessions
Optimize stop loss/take profit logic for dynamic trailing
Explore auto parameter tuning mechanisms

Conclusion

This strategy combines SMA and RSI for a conservative short-term trading approach. Fine-tuning parameters, validating signals, controlling risks makes it more robust and adaptive. There is room for improvement by exploring more SMA combos, adding machine learning models etc. Continuous optimization will lead to further maturity.

Parameters

Name	Default	What it controls
v_input_1	true	From Month
v_input_2	10	From Day
v_input_3	2019	From Year
v_input_4	true	Thru Month
v_input_5	true	Thru Day
v_input_6	2112	Thru Year
v_input_7	true	Show Date Range
v_input_8	9	v_input_8
v_input_9	50	v_input_9
v_input_10	100	v_input_10
v_input_11	70	TP
v_input_12	30	SL

Source Pine Script

/*backtest
start: 2023-08-27 00:00:00
end: 2023-09-26 00:00:00
period: 3h
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/
// © Coinrule

//@version=4
strategy(shorttitle='Maximized Scalping On Trend',title='Maximized Scalping On Trend (by Coinrule)', overlay=true, initial_capital = 1000, process_orders_on_close=true, default_qty_type = strategy.percent_of_equity, default_qty_value = 30, commission_type=strategy.commission.percent, commission_value=0.1)

//Backtest dates
fromMonth = input(defval = 1,    title = "From Month",      type = input.integer, minval = 1, maxval = 12)
fromDay   = input(defval = 10,    title = "From Day",        type = input.integer, minval = 1, maxval = 31)
fromYear  = input(defval = 2019, title = "From Year",       type = input.integer, minval = 1970)
thruMonth = input(defval = 1,    title = "Thru Month",      type = input.integer, minval = 1, maxval = 12)
thruDay   = input(defval = 1,    title = "Thru Day",        type = input.integer, minval = 1, maxval = 31)
thruYear  = input(defval = 2112, title = "Thru Year",       type = input.integer, minval = 1970)

showDate  = input(defval = true, title = "Show Date Range", type = input.bool)

start     = timestamp(fromYear, fromMonth, fromDay, 00, 00)        // backtest start window
finish    = timestamp(thruYear, thruMonth, thruDay, 23, 59)        // backtest finish window
window()  => true      // create function "within window of time"

//MA inputs and calculations
movingaverage_fast = sma(close, input(9))
movingaverage_mid= sma(close, input(50))
movingaverage_slow = sma(close, input (100))


//Trend situation
Bullish= cross(close, movingaverage_fast)

Momentum = movingaverage_mid > movingaverage_slow

// RSI inputs and calculations
lengthRSI = 14
RSI = rsi(close, lengthRSI)

//Entry
strategy.entry(id="long", long = true, when = Bullish and Momentum and RSI > 50)

//Exit

TP = input(70)
SL =input(30)
longTakeProfit  = RSI > TP
longStopPrice = RSI < SL

strategy.close("long", when = longStopPrice or longTakeProfit and window())

plot(movingaverage_fast, color=color.black, linewidth=2 )
plot(movingaverage_mid, color=color.orange, linewidth=2)
plot(movingaverage_slow, color=color.purple, linewidth=2)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-09-27. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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