Trend Following Strategy 427814

Pine ScriptTrend following Machine learning Mean reversion RSI SMA

Author: ChaoZhang
Complexity: Intermediate · 66 lines
Parameters: 10
Backtest bar: 3h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Noro's trend following strategy is a simple trend trading strategy based on price channel, RSI and body filter. It identifies the overall trend using price channel, enters based on RSI overbought/oversold levels, and uses body filter for additional signal confirmation. The strategy suits trending instruments like indices and forex.

Strategy Logic

The key aspects are:

Price channel determines the overall trend. Channel formed by looking back high/low defines uptrend/downtrend.
RSI indicates overbought/oversold for entry timing. RSI above 60 is overbought, below 40 is oversold zone.
Body filter provides final signal. Trades only if candle body exceeds a threshold to avoid noise.
Entries based on combining trend, RSI signal and body filter. Long entries in uptrend on bullish signals, short entries in downtrend on bearish signals.
Optional background colors clearly visualize the trend.
Customizable trading time frames to selectively trade.

Multiple indicators align to create a relatively stable trend following system.

Advantages

The main advantages are:

Price channel intuitively identifies the overall trend direction.
RSI effectively detects overbought/oversold levels for timing entry.
Body filter enhances signal quality and avoids false signals.
Multi-indicator confirmation improves accuracy.
Simple indicators reduce curve fitting risks.
Customizable trading time frames add flexibility.
Easy to use with minimal parameters. Beginner friendly.
Background colors provide visual clarity.

Risks

Some risks to consider:

Price channel trend misidentification risk.
Inaccurate RSI signal risks.
Body filter eliminating valid signals.
Drawdown risk during trend corrections.
Optimization risk from bad parameter tuning.
Position sizing risk from default full allocation.
Instrument selection risk if applied on non-trending assets.
Trading time frame risks if improperly configured.

Enhancement Opportunities

Some enhancement possibilities:

Add stop loss strategy to control loss per trade.
Optimize parameters based on instrument behavior.
Incorporate position sizing rules based on trend strength.
Implement drawdown limits to contain losses.
Add volume price analysis for signal verification.
Introduce machine learning for parameter optimization.
Specialize parameters based on asset class.
Refine trading time frame logic for more flexibility.

Conclusion

Noro's trend following strategy integrates price channel, RSI and body filter into a simple and practical trend trading system. It can trade along the trends and avoid counter-trend trades. With parameter optimization, risk controls and other improvements, the strategy has potential to become a consistently profitable trend trading strategy.

Parameters

Name	Default	What it controls
v_input_1	true	long
v_input_2	true	short
v_input_3	21	MA Period
v_input_4	false	Need trend Background?
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: 2023-08-25 00:00:00
end: 2023-09-24 00:00:00
period: 3h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=2
strategy(title = "Noro's TrendMaster Strategy v1.0", shorttitle = "TrendMaster 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(true, defval = true, title = "short")
len = input(21, defval = 20, minval = 2, maxval = 200, title = "MA Period")
needbg = input(false, defval = false, title = "Need trend Background?")
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")

//PriceChannel 1
lasthigh = highest(close, len)
lastlow = lowest(close, len)
center = (lasthigh + lastlow) / 2

//Trend
trend = low > center and low[1] > center[1] ? 1 : high < center and high[1] < center[1] ? -1 : trend[1]

//Bars
bar = close > open ? 1 : close < open ? -1 : 0

//Fast RSI
fastup = rma(max(change(close), 0), 2)
fastdown = rma(-min(change(close), 0), 2)
rsi = fastdown == 0 ? 100 : fastup == 0 ? 0 : 100 - (100 / (1 + fastup / fastdown))

//Body filter
nbody = abs(close - open)
abody = sma(nbody, 10)
body = nbody > abody / 2

//Signals
up1 = trend == 1 and rsi < 60 and (strategy.position_avg_price > close or strategy.position_size <= 0) and body
dn1 = trend == -1 and rsi > 40 and (strategy.position_avg_price < close or strategy.position_size >= 0) and body

//Lines
plot(center, color = blue, linewidth = 3, transp = 0, title = "MA")

//Background
col = needbg == false ? na : trend == 1 ? lime : red
bgcolor(col, transp = 80)

//Trading

if up1
    strategy.entry("Long", strategy.long, needlong == false ? 0 : na, when=(time > timestamp(fromyear, frommonth, fromday, 00, 00) and time < timestamp(toyear, tomonth, today, 23, 59)))

if dn1
    strategy.entry("Short", strategy.short, needshort == false ? 0 : na, when=(time > timestamp(fromyear, frommonth, fromday, 00, 00) and time < timestamp(toyear, tomonth, today, 23, 59)))
    
if time > timestamp(toyear, tomonth, today, 23, 59)
    strategy.close_all()

Published by ChaoZhang on the FMZ strategy square · last modified 2023-09-25. 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/Trend-Following-Strategy-427814.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.