Dynamic Wave Trend Tracking Strategy

Pine ScriptTrend following Mean reversion Volatility EMA SMA Volume

Author: ChaoZhang
Complexity: Starter · 74 lines
Parameters: 0
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy is a quantitative trading system based on the WaveTrend indicator and trend following. It combines the WaveTrend indicator with moving averages to form a complete trading decision framework. The strategy utilizes EMA and SMA to calculate wave trend values and overall market trends, identifies market turning points through overbought and oversold thresholds, and incorporates trend filters to improve trading accuracy.

Strategy Principle

The strategy's core is implemented through the following steps:

Calculate HLC average price (average of high, low, and closing prices)
Smooth the HLC average using EMA to obtain the ESA line
Calculate and smooth the deviation between HLC average and ESA line using EMA
Calculate K value based on deviation and smooth twice with EMA to get the final TCI line
Use SMA to calculate long-term trend line as trend filter
Generate trading signals when TCI line breaks through overbought/oversold levels and aligns with trend direction

Strategy Advantages

High signal reliability: Effectively reduces false signals by combining WaveTrend indicator and trend filter
Comprehensive risk control: Clear overbought/oversold thresholds for timely stop-loss
Strong adaptability: Strategy parameters can be flexibly adjusted for different market conditions
Clear operational logic: Explicit entry and exit conditions, easy to execute
Comprehensive analysis: Considers both short-term fluctuations and long-term trends, improving trading stability

Strategy Risks

Trend reversal risk: May lag in volatile markets
Parameter sensitivity: Different parameter combinations may lead to drastically different results
Market adaptability: May generate frequent trades in ranging markets
Capital management: Requires reasonable position control to handle market volatility
Technical dependence: Reliance on technical indicators may overlook fundamental factors

Strategy Optimization Directions

Add volatility filter: Adjust trading thresholds during high volatility periods
Incorporate multi-timeframe analysis: Combine signals from different timeframes to improve accuracy
Optimize parameter adaptation: Dynamically adjust indicator parameters based on market conditions
Improve profit/loss management: Add dynamic take-profit and stop-loss mechanisms
Add volume confirmation: Incorporate volume analysis to enhance signal reliability

Summary

The strategy constructs a robust trading system by cleverly combining the WaveTrend indicator with trend filters. While maintaining operational simplicity, it achieves comprehensive market analysis. Although certain risks exist, the strategy has good practical value and development potential through proper risk management and continuous optimization.

Source Pine Script

/*backtest
start: 2019-12-23 08:00:00
end: 2024-12-18 08:00:00
period: 1d
basePeriod: 1d
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © mojomarv

//@version=6
strategy("WaveTrend with Trend Filter", shorttitle="WaveTrend Trend", overlay=false, initial_capital = 100000)

// Inputs for the WaveTrend indicator
inputLength = input.int(10, title="Channel Length", minval=1)
avgLength = input.int(21, title="Average Length", minval=1)
obLevel = input.float(45, title="Overbought Level")
osLevel = input.float(-45, title="Oversold Level")
showSignals = input.bool(true, title="Show Buy/Sell Signals")

// Trend filter input
maLength = input.int(500, title="Trend MA Length", minval=1)

// Calculate WaveTrend values
hlc_avg = (high + low + close) / 3  // Renamed from hlc3 to hlc_avg
esa = ta.ema(hlc_avg, inputLength)
d = ta.ema(math.abs(hlc_avg - esa), inputLength)
k = (hlc_avg - esa) / (0.015 * d)
ci = ta.ema(k, avgLength)
tci = ta.ema(ci, avgLength)

// Moving average for trend detection
trendMA = ta.sma(close, maLength)

// Determine trend
bullishTrend = close > trendMA
bearishTrend = close < trendMA

// Generate signals with trend filter
crossUp = ta.crossover(tci, osLevel)
crossDown = ta.crossunder(tci, obLevel)

// Plot WaveTrend
plot(tci, title="WaveTrend Line", color=color.new(color.blue, 0), linewidth=2)
hline(obLevel, "Overbought", color=color.red, linestyle=hline.style_dotted)
hline(osLevel, "Oversold", color=color.green, linestyle=hline.style_dotted)
hline(0, "Zero Line", color=color.gray, linestyle=hline.style_solid)

// Plot moving average for trend visualization
plot(trendMA, title="Trend MA", color=color.orange, linewidth=1)

// Plot buy and sell signals
plotshape(showSignals and crossUp, title="Buy Signal", location=location.belowbar, style=shape.labelup, color=color.new(color.green, 0), size=size.small)
plotshape(showSignals and crossDown, title="Sell Signal", location=location.abovebar, style=shape.labeldown, color=color.new(color.red, 0), size=size.small)

// Alerts
alertcondition(crossUp, title="Buy Alert", message="WaveTrend Buy Signal (Trend Confirmed)")
alertcondition(crossDown, title="Sell Alert", message="WaveTrend Sell Signal (Trend Confirmed)")
alertcondition(bullishTrend, title="bull", message="WaveTrend Sell Signal (Trend Confirmed)")
alertcondition(bearishTrend, title="bear", message="WaveTrend Sell Signal (Trend Confirmed)")

// Strategy logic
if crossUp and bullishTrend
    strategy.entry("Long", strategy.long)

if crossDown
    strategy.close("Long")

if crossDown and bearishTrend
    strategy.entry("Short", strategy.short)

if crossUp
    strategy.close("Short")

Published by ChaoZhang on the FMZ strategy square · last modified 2024-12-20. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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