Multi Indicator Trend Following Enhanced Quantitative Trading Strategy

Pine ScriptTrend following Machine learning Breakout RSI EMA ATR ADX / DMI

Author: ChaoZhang
Complexity: Starter · 59 lines
Parameters: 0
Backtest bar: 4h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy is a trend-following system that integrates multiple technical indicators including Exponential Moving Average (EMA), Average Directional Index (ADX), and Relative Strength Index (RSI), combined with multi-timeframe analysis. The strategy primarily uses fast and slow EMA crossovers to confirm trend direction, ADX for trend strength filtering, and RSI for momentum analysis, operating on a 1-minute chart for high-frequency trading. Backtesting results show a win rate of 76.92% and a profit factor of 1.819, demonstrating strong profitability.

Strategy Principles

The strategy operates based on the following core mechanisms:

Uses 50-period and 200-period EMAs to identify trend direction, confirming entry signals through fast and slow line crossovers
Employs ADX indicator (14-period) to evaluate trend strength, entering only when ADX is above 25 to avoid choppy markets
Incorporates RSI indicator (14-period) for momentum analysis, considering long positions when RSI is below 30 and short positions above 70
Introduces 4-hour timeframe EMA analysis to enhance trend confirmation through multi-timeframe analysis
Implements dynamic take-profit and stop-loss levels, with take-profit at 5% and stop-loss at 2% for long positions, reversed for shorts

Strategy Advantages

Multiple indicator cross-validation significantly improves signal reliability
Comprehensive risk management including dynamic stop-loss and volatility-based position sizing
Multi-timeframe analysis effectively reduces false breakout risks
High win rate and moderate risk-reward ratio providing good expected returns
Clear strategy logic that is easy to understand and maintain

Strategy Risks

Rapid market movements may render stop-losses ineffective
Ranging markets may generate frequent trades, increasing transaction costs
EMA indicators have inherent lag, potentially missing optimal entry points
Multiple indicators may generate conflicting signals
1-minute timeframe trading requires high execution speed, facing potential slippage risks

Strategy Optimization Directions

Optimize ADX smoothing parameters to improve trend identification accuracy
Implement ATR-based dynamic position sizing for better volatility adaptation
Add volume analysis dimension to enhance signal reliability
Consider market environment classification for different parameter sets under various market conditions
Explore machine learning integration for parameter optimization

Summary

This strategy constructs a robust trend-following system through the synergy of multiple technical indicators. While maintaining a high win rate, it achieves considerable returns through comprehensive risk management mechanisms. Although there is room for optimization, the overall performance is satisfactory, particularly suitable for traders seeking steady returns.

Source Pine Script

/*backtest
start: 2024-02-19 00:00:00
end: 2025-02-17 08:00:00
period: 4h
basePeriod: 4h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy("Enhanced Trend Following Strategy", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=200)

// === INPUTS ===
emaFastLength = input(50, title="Fast EMA Length")
emaSlowLength = input(200, title="Slow EMA Length")
adxLength = input(14, title="ADX Length")
adxSmoothing = input(14, title="ADX Smoothing")
adxThreshold = input(25, title="ADX Threshold")
rsiLength = input(14, title="RSI Length")
rsiOverbought = input(70, title="RSI Overbought Level")
rsiOversold = input(30, title="RSI Oversold Level")

// === INDICATORS ===
emaFast = ta.ema(close, emaFastLength)
emaSlow = ta.ema(close, emaSlowLength)
[dip, dim, adxValue] = ta.dmi(adxLength, adxSmoothing)
rsiValue = ta.rsi(close, rsiLength)

// === MULTI-TIMEFRAME EMA ===
emaFastHTF = request.security(syminfo.tickerid, "240", ta.ema(close, emaFastLength))
emaSlowHTF = request.security(syminfo.tickerid, "240", ta.ema(close, emaSlowLength))

// === CONDITIONS ===
bullishTrend = ta.crossover(emaFast, emaSlow) and adxValue > adxThreshold and rsiValue > rsiOversold
bearishTrend = ta.crossunder(emaFast, emaSlow) and adxValue > adxThreshold and rsiValue < rsiOverbought

// === TRADE EXECUTION ===
if (bullishTrend)
    strategy.entry("Long", strategy.long)
    strategy.exit("TakeProfit_Long", from_entry="Long", limit=close * 1.05, stop=close * 0.98)

if (bearishTrend)
    strategy.entry("Short", strategy.short)
    strategy.exit("TakeProfit_Short", from_entry="Short", limit=close * 0.95, stop=close * 1.02)

// === PLOT INDICATORS ===
plot(emaFast, color=color.blue, title="Fast EMA")
plot(emaSlow, color=color.red, title="Slow EMA")
hline(adxThreshold, "ADX Threshold", color=color.gray, linestyle=hline.style_dotted)

bgcolor(bullishTrend ? color.green : bearishTrend ? color.red : na, transp=90)

// === ALERTS ===
alertcondition(bullishTrend, title="Buy Signal", message="A bullish trend detected!")
alertcondition(bearishTrend, title="Sell Signal", message="A bearish trend detected!")

// === STRATEGY SETTINGS ===
strategy.close("Long", when=rsiValue > rsiOverbought)
strategy.close("Short", when=rsiValue < rsiOversold)

Published by ChaoZhang on the FMZ strategy square · last modified 2025-02-19. 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/Multi-Indicator-Trend-Following-Enhanced-Quantitative-Trading-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.