Short Trading Strategy in Downtrend

This strategy takes advantage of downtrend by building short positions gradually based on moving average and RSI indicators. It aims for profit in falling market.

Pine ScriptTrend following Mean reversion Volatility RSI SMA

Author: ChaoZhang
Complexity: Intermediate · 50 lines
Parameters: 9
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy takes advantage of downtrend by building short positions gradually based on moving average and RSI indicators. It aims for profit in falling market.

Strategy Logic

When close price is below 100-day simple moving average and RSI is greater than 30, go short. Then set stop loss above entry price by 3% and take profit below entry price by 2%. The wider stop loss allows more volatility to avoid unnecessary stops. Close position when price surpasses stop loss or falls below take profit.

On Coinrule platform, set multiple sequential sell orders to build position gradually. When downtrend persists, increase position size. Setting a time interval between orders also helps controlling overall position size.

Each trade is connected with a stop loss order and take profit order. The percentages are optimized for mid-cap coins. You can adjust based on specific coin. As it trades along with trend, stop loss and take profit ratio like 1:1.5 could work.

Stop loss at 3% above entry price Take profit at 2% below entry price A slightly larger stop loss tolerates more volatility.

Advantage Analysis

MA judges trend direction well, catching downtrend in time
RSI filter avoids blindly going short
Gradual position build controls risk maximally with better risk-reward ratio
Stop loss and take profit ensure endurance for each trade

Risk Analysis

Sharp V reversal could lead to major loss
Need to monitor price closely to adjust stop loss and take profit
Leverage needs to be reasonable to control position size
Pausing strategy in choppy market avoids unnecessary loss

Optimization Directions

Test MA with different parameters
Test RSI combinations with different parameters
Adjust stop loss and take profit ratios to optimize risk-reward
Test different time intervals between orders to control position size

Summary

This strategy effectively captures downtrend based on MA and RSI. Gradual position build controls risk while stop loss and take profit ensure endurance. Further optimizing risk-reward ratio by adjusting stop loss/take profit parameters. There is room for improvements on parameters and risk control. But overall a solid short-term short strategy.

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	50	MASignal
v_input_9	14	RSI period

Source Pine Script

/*backtest
start: 2022-10-31 00:00:00
end: 2023-11-06 00:00:00
period: 1d
basePeriod: 1h
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='Short In Downtrend',title='Short In Downtrend Below MA100', overlay=true, initial_capital = 1000, process_orders_on_close=true, default_qty_type = strategy.percent_of_equity, default_qty_value = 100)

//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
inSignal=input(50, title='MASignal')

MA= sma(close, inSignal)

// RSI inputs and calculations
lengthRSI = input(14, title = 'RSI period', minval=1)
RSI = rsi(close, lengthRSI)


//Entry 
strategy.entry(id="short", long = false, when = close < MA and RSI > 30)

//Exit
shortStopPrice  = strategy.position_avg_price * (1 + 0.03)
shortTakeProfit = strategy.position_avg_price * (1 - 0.02)

strategy.close("short", when = close > shortStopPrice or close < shortTakeProfit and window())


plot(MA, color=color.purple, linewidth=2)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-11-07. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

Build this with AgenKit

A spec is not a trading system. Paste this into your coding agent and AgenKit runs a specialist team — architecture, test-first build, risk gates, review — with paper trading on by default.

in Claude Code, Cursor, Codex & 4 more

/agenkit build a production-grade trading bot with a backtest engine, risk controls and paper trading by default from strategies/Short-Trading-Strategy-in-Downtrend.md

npx agenkit install engineering-kit in your project
Paste the command into your agent and approve each phase
Get tests, backtest, risk controls and a paper-trading switch

Get AgenKit How /agenkit works

Understand it first

Before you run this, read the lesson behind the idea and how to backtest it honestly. Specs like this one are the survivors of an unknown number of trials; deflate their results accordingly.