Multi Timeframe Momentum Trend Tracking and Risk Management Quantitative Trading Strategy

Pine ScriptMomentum Trend following Multi-timeframe RSI SMA Stochastic

Author: ianzeng123
Complexity: Starter · 87 lines
Parameters: 0
Backtest bar: 1d · swing
Tested on: TRX/USD · futures

Jump to code View in vault Original on FMZ

Overview

This is a quantitative trading strategy based on multi-timeframe technical indicator combinations, achieving precise market entry and risk control through comprehensive analysis of moving averages, Stochastic Relative Strength Index (SRI), and price momentum. The strategy aims to capture market trends while effectively managing trading risks, suitable for quantitative traders seeking stable returns.

Strategy Principles

The strategy's core consists of five key technical indicators:

Moving Average Indicators:

5-day, 10-day, 50-day, and 100-day Simple Moving Averages (SMA)
Determine market trend direction through relative positions of moving averages
Confirm entry signals based on price and moving average relationships

Stochastic Relative Strength Index (SRI):

Calculated using 1-minute timeframe
SRI below 70 as long entry signal
SRI above 30 as short entry signal

Candlestick Patterns:

Analyze opening price relative to previous candle's close
Determine current price momentum and market sentiment

Risk Management Mechanism:

Set Take Profit (TP) and Stop Loss (SL) points
Implement Break-Even (BE) strategy
Dynamically adjust stop loss positions

Strategy Advantages

Multi-Dimensional Signal Verification

Comprehensive use of moving averages, SRI, and price momentum
Significantly reduce false signal probability
Improve trading signal reliability

Flexible Risk Control

Preset take profit and stop loss points
Dynamic break-even mechanism
Effectively control maximum loss per trade

Multi-Timeframe Analysis

Combine moving averages of different periods
Comprehensively capture market trends
Enhance strategy adaptability

Parameter Adjustability

Customizable take profit and stop loss points
Adapt to different market environments and trading varieties

Strategy Risks

Parameter Sensitivity Risk

Moving average and SRI parameters significantly impact strategy performance
Require comprehensive backtesting and parameter optimization

Extreme Market Volatility Risk

Strategy may fail under extreme market conditions
Recommend setting maximum drawdown limits

Over-Trading Risk

Frequent trading may increase transaction costs
Need adjustment based on actual trading expenses

Indicator Lagging Risk

Moving averages have inherent lag
May miss early-stage trend signals

Strategy Optimization Directions

Introduce Machine Learning Algorithms

Use supervised learning to optimize parameters
Dynamically adjust take profit and stop loss points
Enhance strategy adaptability

Add Additional Filtering Conditions

Introduce volume indicators
Incorporate trend strength indicators
Improve signal accuracy

Multi-Variety Adaptability Optimization

Develop universal parameter adaptation mechanism
Reduce manual intervention
Improve strategy universality

Summary

This is a quantitative trading strategy based on multi-timeframe analysis, aiming to capture market trends and control trading risks through comprehensive technical indicators and advanced risk management mechanisms. The strategy's core advantages lie in multi-dimensional signal verification and flexible risk control. Future improvements will focus on enhancing stability and return rates through machine learning and more complex technical indicator combinations.

Source Pine Script

/*backtest
start: 2024-04-17 00:00:00
end: 2025-04-15 08:00:00
period: 1d
basePeriod: 1d
exchanges: [{"eid":"Futures_Binance","currency":"TRX_USD"}]
*/

//@version=6
strategy("Strategia LONG & SHORT con TP, SL e BE", overlay=true, default_qty_type=strategy.fixed, default_qty_value=1)

// === INPUT === //
tp_points = input.int(60000, "Take Profit (punti)")
sl_points = input.int(25000, "Stop Loss (punti)")
breakeven_trigger = tp_points * 0.5

// === MEDIE MOBILI === //
ma5  = ta.sma(close, 5)
ma10 = ta.sma(close, 10)
ma50 = ta.sma(close, 50)
ma100 = ta.sma(close, 100)

// === SRI da timeframe 1 minuto === //
sri_tf = "1"
sri_length = 10
sri_src = close
sri = request.security(syminfo.tickerid, sri_tf, ta.stoch(sri_src, sri_src, sri_src, sri_length))

// === CONDIZIONI LONG === //
long_candle        = open > close[1]
price_above_ma100  = close > ma100
ma50_above_ma100   = ma50 > ma100
ma5_above_ma10     = ma5 > ma10
sri_below_75       = sri < 70

long_condition = long_candle and price_above_ma100 and ma50_above_ma100 and ma5_above_ma10 and sri_below_75

// === CONDIZIONI SHORT === //
short_candle       = open < close[1]
price_below_ma100  = close < ma100
ma50_below_ma100   = ma50 < ma100
ma5_below_ma10     = ma5 < ma10
sri_above_25       = sri > 30

short_condition = short_candle and price_below_ma100 and ma50_below_ma100 and ma5_below_ma10 and sri_above_25

// === ENTRY LONG === //
if (long_condition)
    strategy.entry("Long", strategy.long)

// === ENTRY SHORT === //
if (short_condition)
    strategy.entry("Short", strategy.short)

// === GESTIONE USCITE === //
var float long_entry_price  = na
var float short_entry_price = na

// LONG: TP/SL + break-even
if (strategy.position_size > 0)
    if (na(long_entry_price))
        long_entry_price := strategy.position_avg_price

    tp_price_long = long_entry_price + tp_points * syminfo.mintick
    sl_price_long = long_entry_price - sl_points * syminfo.mintick
    be_trigger_long = long_entry_price + breakeven_trigger * syminfo.mintick
    sl_be = close >= be_trigger_long ? long_entry_price : sl_price_long

    strategy.exit("Exit Long", from_entry="Long", limit=tp_price_long, stop=sl_be)

// SHORT: TP/SL + break-even
if (strategy.position_size < 0)
    if (na(short_entry_price))
        short_entry_price := strategy.position_avg_price

    tp_price_short = short_entry_price - tp_points * syminfo.mintick
    sl_price_short = short_entry_price + sl_points * syminfo.mintick
    be_trigger_short = short_entry_price - breakeven_trigger * syminfo.mintick
    sl_be_short = close <= be_trigger_short ? short_entry_price : sl_price_short

    strategy.exit("Exit Short", from_entry="Short", limit=tp_price_short, stop=sl_be_short)

// Reset quando flat
if (strategy.position_size == 0)
    long_entry_price := na
    short_entry_price := na

Published by ianzeng123 on the FMZ strategy square · last modified 2025-04-17. 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-Timeframe-Momentum-Trend-Tracking-and-Risk-Management-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.