Multi Technical Indicator Moving Average Crossover Trend Following Quantitative Trading Strategy

Pine ScriptTrend following Mean reversion Momentum RSI MACD EMA SMA Bollinger Bands Stochastic

Author: ianzeng123
Complexity: Starter · 56 lines
Parameters: 0
Backtest bar: 1h · intraday
Tested on: TRB/USDT · spot

Jump to code View in vault Original on FMZ

Overview

This strategy is a trend-following trading system based on multiple technical indicators, integrating Moving Average (MA), Relative Strength Index (RSI), Bollinger Bands (BB), Moving Average Convergence Divergence (MACD), and Stochastic oscillator. It identifies market trends and trading opportunities through cross-confirmation between indicators. The strategy employs percentage-based position management, using 1% of funds for each trade by default.

Strategy Principle

The strategy determines trading signals through the following dimensions:

Uses 14-period Simple Moving Average (SMA) as trend indicator baseline
RSI indicator for overbought/oversold conditions, with 30 and 70 as key thresholds
Bollinger Bands for price volatility range, with 20-period
MACD indicator (12,26,9) for trend confirmation
Stochastic oscillator (14,3) for momentum judgment

Long conditions must simultaneously satisfy:

RSI below 30 (oversold)
MACD line crosses above signal line
Stochastic K value below 20
Closing price above Bollinger Band middle line
Previous closing price below Bollinger Band lower line

Short conditions must simultaneously satisfy:

RSI above 70 (overbought)
MACD line crosses below signal line
Stochastic K value above 80
Closing price below Bollinger Band middle line
Previous closing price above Bollinger Band upper line

Strategy Advantages

Multiple technical indicator cross-confirmation effectively filters false signals
Combines trend-following and oscillating indicators, suitable for both trending and reversal markets
Percentage-based position management effectively controls risk
Adjustable indicator parameters provide good adaptability
Clear trading signals, easy to execute and backtest

Strategy Risks

Multiple indicators may lead to signal lag, affecting entry timing
Frequent trading in oscillating markets increases costs
Fixed parameters perform differently in various market environments
Technical indicators may contradict each other, causing signal confusion Suggested risk mitigation measures:

Dynamically adjust parameters based on different market characteristics
Set stop-loss and take-profit levels to control risk
Combine with other indicators like volume for signal confirmation
Regularly evaluate strategy performance and adjust timely

Strategy Optimization Directions

Introduce adaptive parameter mechanism to dynamically adjust indicator parameters based on market volatility
Add volume indicators as auxiliary confirmation
Optimize position management, consider gradual position building and reduction
Add market environment recognition module to adopt different strategies in different market conditions
Introduce machine learning algorithms to optimize signal generation logic

Summary

This strategy establishes a relatively complete trend-following trading system through the comprehensive use of multiple technical indicators. The strategy features reliable signals and controllable risk, but still needs continuous parameter and logic optimization in live trading based on market conditions. Through continuous improvement and refinement, this strategy has the potential to achieve stable returns in different market environments.

Source Pine Script

/*backtest
start: 2024-02-21 00:00:00
end: 2025-02-18 08:00:00
period: 1h
basePeriod: 1h
exchanges: [{"eid":"Binance","currency":"TRB_USDT"}]
*/

//@version=5
strategy("TradingBot Strategy", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=1)

// Input parameters
lotSize = input.float(0.1, title="Lot Size")
maPeriod = input.int(14, title="MA Period")
rsiPeriod = input.int(14, title="RSI Period")
bbPeriod = input.int(20, title="Bollinger Bands Period")
macdFast = input.int(12, title="MACD Fast EMA")
macdSlow = input.int(26, title="MACD Slow EMA")
macdSignal = input.int(9, title="MACD Signal SMA")
stochK = input.int(14, title="Stochastic %K")
stochD = input.int(3, title="Stochastic %D")

// Indicators
ma = ta.sma(close, maPeriod)
rsi = ta.rsi(close, rsiPeriod)
[bbUpper, bbMiddle, bbLower] = ta.bb(close, bbPeriod, 2)
[macdLine, signalLine, _] = ta.macd(close, macdFast, macdSlow, macdSignal)
k = ta.stoch(close, high, low, stochK)
d = ta.sma(k, stochD)

// Plot indicators
plot(ma, color=color.blue, title="MA", linewidth=1)
hline(70, "RSI Overbought", color=color.red)
hline(30, "RSI Oversold", color=color.green)
plot(rsi, color=color.purple, title="RSI", linewidth=1)
plot(bbUpper, color=color.orange, title="Bollinger Bands Upper", linewidth=1)
plot(bbMiddle, color=color.gray, title="Bollinger Bands Middle", linewidth=1)
plot(bbLower, color=color.orange, title="Bollinger Bands Lower", linewidth=1)
hline(0, "MACD Zero", color=color.gray)
plot(macdLine, color=color.blue, title="MACD Line", linewidth=1)
plot(signalLine, color=color.red, title="MACD Signal Line", linewidth=1)
hline(80, "Stochastic Overbought", color=color.red)
hline(20, "Stochastic Oversold", color=color.green)
plot(k, color=color.blue, title="Stochastic %K", linewidth=1)
plot(d, color=color.red, title="Stochastic %D", linewidth=1)

// Trading logic
longCondition = rsi < 30 and macdLine > signalLine and k < 20 and close > bbMiddle and close[1] < bbLower
shortCondition = rsi > 70 and macdLine < signalLine and k > 80 and close < bbMiddle and close[1] > bbUpper

if (longCondition)
    strategy.entry("Buy", strategy.long, qty=lotSize)
    label.new(bar_index, low, text="BUY", style=label.style_label_up, color=color.green, textcolor=color.white, size=size.small, yloc=yloc.belowbar)
if (shortCondition)
    strategy.entry("Sell", strategy.short, qty=lotSize)
    label.new(bar_index, high, text="SELL", style=label.style_label_down, color=color.red, textcolor=color.white, size=size.small, yloc=yloc.abovebar)

Published by ianzeng123 on the FMZ strategy square · last modified 2025-02-20. 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/Multi-Technical-Indicator-Moving-Average-Crossover-Trend-Following-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.