Extremum Reversion Tracking Strategy

The extremum reversion tracking strategy tracks the extremum points of price fluctuation range and makes reversal long/short positions at extremum points to track trends.

Pine ScriptMachine learning Mean reversion Trend following

Author: ChaoZhang
Complexity: Intermediate · 51 lines
Parameters: 10
Backtest bar: 6h · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

The extremum reversion tracking strategy tracks the extremum points of price fluctuation range and makes reversal long/short positions at extremum points to track trends.

Strategy Principle

The strategy is mainly based on the following principles:

Use security function to obtain high and low prices of different cycle K-lines to detect whether they are equal to previous ones, so as to judge if new extremum points are reached.
When new extremum points are detected, make short position if it is currently a bull market, and make long position if it is currently a bear market.
Set stop loss point as the new extremum point formed after long/short position is made to track trends with stop loss.
Set the effective time range of the strategy by configuring start year, month and date to make adjustments for different time periods.

Advantages

The main advantages of this strategy are:

Effectively capture extremum points of price changes and make reversal positions to track trends.
Configure time and risk management to control usage time and capital of the strategy to reduce risks.
Use new extremum points as stop loss points to dynamically adjust stop loss positions based on new price fluctuation range.
Simple and clear strategy logic for easy understanding, debugging and optimization.

Risks

There are also some risks for this strategy:

There could be misjudgement in determining extremum points, causing errors in long/short positions. The logic can be optimized.
Stop loss position is close to entry point, increasing the probability of stop loss being triggered. Floating stop loss can be used.
No consideration on pyramiding positions along trends and reverse positions, less profitable in trending markets. Related logics can be added.
Currency and time range configuration is quite rigid, cannot make dynamic adjustments. Parameter optimization system can be introduced.

Optimization Directions

The strategy can be optimized in the following aspects:

Optimize extremum point logic with more filters to avoid misjudgement.
Add floating stop loss mechanism based on price and volatility changes to adjust stop loss distance.
Introduce pyramiding and reverse position modules based on trends and volatility to improve profitability.
Establish parameter optimization mechanism for automatic testing and parameter tuning.
Incorporate machine learning models to assist strategy decision making.

Summary

The extremum reversion tracking strategy works by capturing price extremums and tracking trends, adaptable and profitable. Further optimizations on extremum judgement, stop loss mechanism, position opening rules etc. could shape it into a solid quantitative trading strategy.

Parameters

Name	Default	What it controls
v_input_1	true	Long
v_input_2	false	Short
v_input_3	100	Capital, %
v_input_4	W	Timeframe for extremums
v_input_5	1900	From Year
v_input_6	2100	To Year
v_input_7	true	From Month
v_input_8	12	To Month
v_input_9	true	From day
v_input_10	31	To day

Source Pine Script

/*backtest
start: 2024-01-01 00:00:00
end: 2024-01-31 23:59:59
period: 6h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//Noro
//2018

//@version=2
strategy(title = "Noro's Extremum Strategy v1.0", shorttitle = "Extremum str 1.0", 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 = "Capital, %")
tf = input('W', title = 'Timeframe for extremums')
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")

//Levels
highm = request.security(syminfo.tickerid, tf, high[1])
lowm = request.security(syminfo.tickerid, tf, low[1])
upcolorm = highm == highm[1] ? lime : na
dncolorm = lowm == lowm[1] ? red : na
plot(highm, color = upcolorm, linewidth = 3)
plot(lowm, color = dncolorm, linewidth = 3)

//Signals
size = strategy.position_size
up = size > 0 ? highm * 1000000 : highm != highm[1] ? highm : up[1]
dn = size < 0 ? 0 : lowm != lowm[1] ? lowm : dn[1]
exit = true

//Trading
lot = strategy.position_size != strategy.position_size[1] ? strategy.equity / close * capital / 100 : lot[1]

if highm > 0 and high[1] < highm and highm == highm[1]
    strategy.entry("Long", strategy.long, needlong == false ? 0 : lot, stop = up)
    
if lowm > 0 and low[1] > lowm and lowm == lowm[1]
    strategy.entry("Short", strategy.short, needshort == false ? 0 : lot, stop = dn)

if exit
    strategy.close_all()

Published by ChaoZhang on the FMZ strategy square · last modified 2024-02-20. 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/Extremum-Reversion-Tracking-Strategy.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.