Automatic Long Short Trading Strategy Based on Daily Pivot Points

Pine ScriptMachine learning Mean reversion Trend following Bollinger Bands Pivots / S&R

Author: ChaoZhang
Complexity: Intermediate · 52 lines
Parameters: 12
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy draws two lines based on the highest and lowest prices of daily candlesticks for judgments of long/short trends. It goes long when price breaks through the highest price line and goes short when price breaks through the lowest price line. It can automatically switch between long and short positions.

Strategy Logic

This strategy mainly utilizes the pivot points of daily candlesticks to determine long/short trends. The so-called "pivot points" refer to yesterday's highest and lowest prices. These two lines form a trading range. If today's price breaks through either of them, it indicates a reversal of the trend.

Specifically, the main logic is as follows:

Highest price line: Plot yesterday's highest price level. A break-through signals long trend.
Lowest price line: Plot yesterday's lowest price level. A break-through signals short trend.
Long entry: Open long position when closing price breaks through highest price line.
Short entry: Open short position when closing price breaks through lowest price line.
Stop loss: Long stop loss near lowest price line, short stop loss near highest price line.

By capturing trends through break-throughs of highest/lowest prices, it realizes automatic switching between long and short.

Advantage Analysis

The main advantages of this strategy are:

Simple logic, easy to understand and implement
Based on daily bars, long cycle, less susceptible to short-term noises
Automatic switch between long and short, avoid non-trending markets
Clear stop loss, beneficial for risk control

Risk Analysis

Some risks:

Daily bars have lower frequency, unable to stop loss timely
Fake break-throughs may cause unnecessary losses
Long holding may lead to expanded losses

Improvements:

Add other higher frequency indicators for confirmation
Optimize parameters to filter out fake break-throughs
Adopt progressive stop loss methods for timely stop loss

Optimization Directions

Some directions:

More backtesting on different products and longer datasets to test stability
Explore other break-through indicators like channels, Bollinger Bands etc.
Incorporate trading volume to avoid false breaks without volume
Add more filters to reduce false breaks
Utilize machine learning for parameter optimization

Summary

In summary, this simple strategy realizes auto long/short based on daily pivots. The logic is clear and easy to understand. Further optimizations can improve stability. Investors can apply it to live trading based on personal risk preference.

Parameters

Name	Default	What it controls
v_input_1	true	Long
v_input_2	false	Short
v_input_3	100	Lot
v_input_4	true	Show lines
v_input_5	false	Show background
v_input_6	false	Show new day
v_input_7	1900	From Year
v_input_8	2100	To Year
v_input_9	true	From Month
v_input_10	12	To Month
v_input_11	true	From day
v_input_12	31	To day

Source Pine Script

/*backtest
start: 2023-12-01 00:00:00
end: 2023-12-31 23:59:59
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//Noro
//2019

//@version=3
strategy(title = "Noro's DEX Strategy", shorttitle = "DEX str", overlay = true, default_qty_type = strategy.percent_of_equity, default_qty_value = 100, pyramiding = 0)

//Settings
needlong = input(true, defval = true, title = "Long")
needshort = input(false, defval = false, title = "Short")
capital = input(100, defval = 100, minval = 1, maxval = 10000, title = "Lot")
showlines = input(true, title = "Show lines")
showbg = input(false, title = "Show background")
showday = input(false, title = "Show new day")
fromyear = input(1900, defval = 1900, minval = 1900, maxval = 2100, title = "From Year")
toyear = input(2100, defval = 2100, minval = 1900, maxval = 2100, title = "To Year")
frommonth = input(01, defval = 01, minval = 01, maxval = 12, title = "From Month")
tomonth = input(12, defval = 12, minval = 01, maxval = 12, title = "To Month")
fromday = input(01, defval = 01, minval = 01, maxval = 31, title = "From day")
today = input(31, defval = 31, minval = 01, maxval = 31, title = "To day")

//New day trand
bar = close > open ? 1 : close < open ? -1 : 0
newday = request.security(syminfo.tickerid, 'D', time)

//Lines
uplevel = request.security(syminfo.tickerid, 'D', high)
dnlevel = request.security(syminfo.tickerid, 'D', low)
upcolor = uplevel == uplevel[1] and showlines ? lime : na
dncolor = dnlevel == dnlevel[1] and showlines? red : na
plot(uplevel, offset = 1, linewidth = 2, color = upcolor)
plot(dnlevel, offset = 1, linewidth = 2, color = dncolor)

//Background
size = strategy.position_size
col = time == newday + 86400000 and showday ? blue : showbg and size > 0 ? lime : showbg and size < 0 ? red : na
bgcolor(col)

//Orders
lot = 0.0
lot := size != size[1] ? strategy.equity / close * capital / 100 : lot[1]
truetime = true
if uplevel > 0 and dnlevel > 0
    strategy.entry("Long", strategy.long, needlong ? lot : 0, stop = uplevel, when = truetime)
    strategy.entry("Close", strategy.short, needshort ? lot : 0, stop = dnlevel, when = truetime)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-23. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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