Adaptive Weighted Trend Following Strategy VIDYA Multi Indicator System

Pine ScriptTrend following Momentum Volatility

Author: ChaoZhang
Complexity: Starter · 59 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 trend following trading system based on the VIDYA (Variable Index Dynamic Average) indicator. The strategy adapts to market volatility by dynamically adjusting weights, combining Chande's Momentum Oscillator (CMO) and Standard Deviation (StDev) calculation methods to achieve more accurate trend identification and trading signal generation. The system introduces an adaptive mechanism on top of traditional moving averages, automatically adjusting sensitivity based on market conditions.

Strategy Principles

The core of the strategy is the VIDYA indicator, with calculation process including these key steps:

Setting basic period (default 21) and smoothing coefficient alpha
Incorporating CMO or StDev as volatility calculation methods
Using dynamic weight k to adjust VIDYA's sensitivity to price changes
Generating long signals when VIDYA crosses upward and short signals when crossing downward

The strategy allows users to choose between CMO or standard deviation for volatility coefficient calculation, increasing flexibility. CMO mode uses a fixed 9-period cycle, while StDev mode maintains consistency with the base period.

Strategy Advantages

Strong Adaptability: Maintains good performance in different market environments through dynamic weight adjustment
Stable Signals: Better filters false signals compared to traditional moving averages
Adjustable Parameters: Provides multiple tunable parameters for optimization based on different market characteristics
Dual Calculation Methods: Supports both CMO and StDev volatility calculations, increasing strategy adaptability
User-Friendly: Clear strategy logic and definitive signals, convenient for practical operation

Strategy Risks

Trend Dependency: May generate frequent false signals in oscillating markets
Parameter Sensitivity: Different parameter combinations significantly affect strategy performance
Lag: Inherent delay exists as a moving average type indicator
Market Adaptability: May underperform in certain specific market environments
Money Management: Lack of stop-loss mechanism may lead to significant drawdowns

Strategy Optimization Directions

Introduce Volatility Filter: Adjust signal generation rules in high volatility environments
Add Trend Confirmation Indicators: Combine with other technical indicators to improve signal reliability
Improve Money Management: Design dynamic stop-loss and position management mechanisms
Optimize Parameter Selection: Develop automatic parameter optimization methods for different market cycles
Enhance Market Environment Assessment: Dynamically adjust strategy parameters based on market conditions

Summary

The VIDYA strategy provides a relatively reliable trend following solution through innovative adaptive weight mechanisms. While maintaining simplicity and ease of use, the strategy improves adaptability to market changes through dynamic adjustments. Although some inherent limitations exist, the provided optimization directions can further enhance strategy stability and reliability. The dual calculation methods offer greater flexibility for application in different market environments.

Source Pine Script

/*backtest
start: 2019-12-23 08:00:00
end: 2024-12-04 00:00:00
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/
// © GriffinJames


//@version=5
strategy("VIDYA Strategy", overlay=true, initial_capital=25000)

// Inputs
src = input(close, title="Source")
pds = input.int(21, title="Length")
fixCMO = input.bool(true, title="Fixed CMO Length (9)?")
select = input.bool(true, title="Calculation Method: CMO/StDev?")
alpha = 2 / (pds + 1)
momm = ta.change(src)

// Functions to calculate MOM
f1(m) => m >= 0.0 ? m : 0.0
f2(m) => m >= 0.0 ? 0.0 : -m

m1 = f1(momm)
m2 = f2(momm)
sm1 = fixCMO ? math.sum(m1, 9) : math.sum(m1, pds)
sm2 = fixCMO ? math.sum(m2, 9) : math.sum(m2, pds)

percent(nom, div) => 100 * nom / div
chandeMO = na(percent(sm1 - sm2, sm1 + sm2)) ? 0 : percent(sm1 - sm2, sm1 + sm2)

// Select calculation method
k = select ? math.abs(chandeMO) / 100 : ta.stdev(src, pds)

// Calculate VIDYA
var float VIDYA = na
VIDYA := na(VIDYA[1]) ? src : alpha * k * src + (1 - alpha * k) * VIDYA[1]

// Conditions for long and short
col12 = VIDYA > VIDYA[1]
col32 = VIDYA < VIDYA[1]

// Plot VIDYA with dynamic colors
color2 = col12 ? color.new(color.blue, 0) : col32 ? color.new(color.maroon, 0) : color.new(color.blue, 0)
plot(VIDYA, "VAR", color=color2, linewidth=2)

// Long and Short Strategy
if (col12)
    strategy.entry("Go Long", strategy.long)
if (col32)
    strategy.entry("Go Short", strategy.short)

// Alert for VIDYA color change
alertcondition(ta.cross(VIDYA, VIDYA[1]), title="Color ALARM!", message="VIDYA has changed color!")

Published by ChaoZhang on the FMZ strategy square · last modified 2024-12-05. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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