Multi Indicator Trend Following and Volatility Breakout Strategy

Pine ScriptBreakout Trend following Volatility RSI EMA SMA ATR Ichimoku Stochastic

Author: ChaoZhang
Complexity: Intermediate · 122 lines
Parameters: 0
Backtest bar: 2h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This is a comprehensive trading strategy that combines trend following and volatility breakout approaches using multiple technical indicators. The strategy integrates an EMA system, ADX for trend strength, ATR for volatility measurement, OBV for volume analysis, and supplementary indicators like Ichimoku Cloud and Stochastic oscillator to capture market trends and breakout opportunities. A time filter is implemented to optimize trading efficiency by operating only during specific trading hours.

Strategy Principle

The core logic is based on multi-layer technical analysis:

Trend following system using 50 and 200 period EMAs
Trend strength confirmation through ADX
Additional trend validation using Ichimoku Cloud
Overbought/oversold identification with Stochastic oscillator
Dynamic stop-loss and profit targets using ATR
Volume confirmation through OBV

Buy signals are generated when:

Within allowed trading hours
Price above short-term EMA
Short-term EMA above long-term EMA
ADX above threshold
Price above Ichimoku Cloud
Stochastic in oversold territory

Strategy Advantages

Multiple indicator cross-validation improves signal reliability
Combination of trend following and volatility breakout increases adaptability
Time filter avoids inefficient trading periods
Dynamic stop-loss and profit targets adapt to market volatility
Integrated volume-price analysis provides comprehensive market view
Systematic entry/exit rules reduce subjective judgment

Strategy Risks

Multiple indicators may lead to lagging signals
False signals in ranging markets
Complex parameter optimization with overfitting risks
Time restrictions may miss important market moves
Wide stops may result in larger individual losses

Risk control suggestions:

Regular parameter optimization review
Consider adding volatility filters
Implement stricter money management rules
Add supplementary trend confirmation indicators

Strategy Optimization Directions

Introduce adaptive parameter system for dynamic indicator adjustment
Add market regime classification for different signal generation rules
Optimize time filter based on historical data analysis
Improve stop-loss strategy with trailing stops
Incorporate market sentiment indicators for signal quality enhancement

Summary

The strategy constructs a complete trading system through the comprehensive application of multiple technical indicators. Its strengths lie in multi-layer indicator cross-validation and strict risk control, while facing challenges in parameter optimization and signal lag. Through continuous optimization and improvement, the strategy shows potential for stable performance across different market conditions.

Source Pine Script

/*backtest
start: 2024-11-11 00:00:00
end: 2024-12-10 08:00:00
period: 2h
basePeriod: 2h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy("Khaleq Strategy Pro - Fixed Version", overlay=true)

// === Input Settings ===
ema_short = input.int(50, "EMA Short", minval=1)
ema_long = input.int(200, "EMA Long", minval=1)
adx_threshold = input.int(25, "ADX Threshold", minval=1)
atr_multiplier = input.float(2.0, "ATR Multiplier", minval=0.1)
time_filter_start = input(timestamp("0000-01-01 09:00:00"), "Trading Start Time", group="Time Filter")
time_filter_end = input(timestamp("0000-01-01 17:00:00"), "Trading End Time", group="Time Filter")

// === Ichimoku Settings ===
tenkan_len = 9
kijun_len = 26
senkou_span_b_len = 52
displacement = 26

// === Calculations ===
// Ichimoku Components
tenkan_sen = (ta.highest(high, tenkan_len) + ta.lowest(low, tenkan_len)) / 2
kijun_sen = (ta.highest(high, kijun_len) + ta.lowest(low, kijun_len)) / 2
senkou_span_a = (tenkan_sen + kijun_sen) / 2
senkou_span_b = (ta.highest(high, senkou_span_b_len) + ta.lowest(low, senkou_span_b_len)) / 2

// EMA Calculations
ema_short_val = ta.ema(close, ema_short)
ema_long_val = ta.ema(close, ema_long)

// Manual ADX Calculation
length = 14
dm_plus = math.max(ta.change(high), 0)
dm_minus = math.max(-ta.change(low), 0)
tr = math.max(high - low, math.max(math.abs(high - close[1]), math.abs(low - close[1])))
tr14 = ta.sma(tr, length)
dm_plus14 = ta.sma(dm_plus, length)
dm_minus14 = ta.sma(dm_minus, length)
di_plus = (dm_plus14 / tr14) * 100
di_minus = (dm_minus14 / tr14) * 100
dx = math.abs(di_plus - di_minus) / (di_plus + di_minus) * 100
adx_val = ta.sma(dx, length)

// ATR Calculation
atr_val = ta.atr(14)

// Stochastic RSI Calculation
k = ta.stoch(close, high, low, 14)
d = ta.sma(k, 3)

// Time Filter
is_within_time = true

// Support and Resistance (High and Low Levels)
resistance_level = ta.highest(high, 20)
support_level = ta.lowest(low, 20)

// Volume Analysis (On-Balance Volume)
vol_change = ta.change(close)
obv = ta.cum(vol_change > 0 ? volume : vol_change < 0 ? -volume : 0)

// === Signal Conditions ===
buy_signal = is_within_time and
             (close > ema_short_val) and
             (ema_short_val > ema_long_val) and
             (adx_val > adx_threshold) and
             (close > senkou_span_a) and
             (k < 20)  // Stochastic oversold

sell_signal = is_within_time and
              (close < ema_short_val) and
              (ema_short_val < ema_long_val) and
              (adx_val > adx_threshold) and
              (close < senkou_span_b) and
              (k > 80)  // Stochastic overbought

// === Plotting ===
// Plot Buy and Sell Signals
plotshape(buy_signal, color=color.green, style=shape.labelup, title="Buy Signal", location=location.belowbar, text="BUY")
plotshape(sell_signal, color=color.red, style=shape.labeldown, title="Sell Signal", location=location.abovebar, text="SELL")

// Plot EMAs
plot(ema_short_val, color=color.blue, title="EMA Short")
plot(ema_long_val, color=color.orange, title="EMA Long")

// Plot Ichimoku Components
plot(senkou_span_a, color=color.green, title="Senkou Span A", offset=displacement)
plot(senkou_span_b, color=color.red, title="Senkou Span B", offset=displacement)

// // Plot Support and Resistance using lines
// var line resistance_line = na
// var line support_line = na
// if bar_index > 1
//     line.delete(resistance_line)
//     line.delete(support_line)
// resistance_line := line.new(x1=bar_index - 1, y1=resistance_level, x2=bar_index, y2=resistance_level, color=color.red, width=1, style=line.style_dotted)
// support_line := line.new(x1=bar_index - 1, y1=support_level, x2=bar_index, y2=support_level, color=color.green, width=1, style=line.style_dotted)

// Plot OBV
plot(obv, color=color.purple, title="OBV")

// Plot Background for Trend (Bullish/Bearish)
bgcolor(close > ema_long_val ? color.new(color.green, 90) : color.new(color.red, 90), title="Trend Background")

// === Alerts ===
alertcondition(buy_signal, title="Buy Alert", message="Buy Signal Triggered")
alertcondition(sell_signal, title="Sell Alert", message="Sell Signal Triggered")

// === Strategy Execution ===
if buy_signal
    strategy.entry("Buy", strategy.long)

if sell_signal
    strategy.close("Buy")
    strategy.exit("Sell", "Buy", stop=close - atr_multiplier * atr_val, limit=close + atr_multiplier * atr_val)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-12-12. 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-and-Volatility-Breakout-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.