Dual Stochastics and Volume Weighted Moving Average Combination Indicator Strategy

This is a strategy that utilizes a combination of dual Stochastics indicators and Volume Weighted Moving Average to identify trends. It uses two Stochastics indicators with different periods, one short-term and one…

Pine ScriptBreakout Mean reversion Trend following SMA Stochastic

Author: ChaoZhang
Complexity: Starter · 55 lines
Parameters: 3
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

The strategy mainly implements trend identification through the following parts:

Calculate a short-period Stochastics indicator with period length input(30) and smooth parameter 2
Calculate a long-period Stochastics indicator with period length input(90) and smooth parameter 2
Add the short-period and long-period Stochastics together to get a combined Stochastics curve ts
Calculate a Volume Weighted Moving Average of ts curve with period length input(30)
Compare current tsl value with its value 1 period ago, when tsl rises, it indicates an uptrend, when tsl falls, it indicates a downtrend
Combine with Stochastics curve position to identify bullish or bearish signals

When tsl rises and ts is in middle zone, it is a bullish signal
When tsl falls and ts is in middle zone, it is a bearish signal

Advantage Analysis

The strategy combines trend identification and overbought-oversold analysis, which can identify trend direction quite reliably. The advantages are:

The dual Stochastics can reflect both short-term and long-term overbought/oversold situations, avoiding missing some signals
Volume weighted moving average can filter out some false breakout signals
Stochastics curve position re-confirms the reliability of trend signals
Adjustable parameters suit different markets
Clear and simple logic, easy to understand and modify

Risks and Improvements

There are also some risks to note for this strategy:

Stochastics may give false signals, needs filtering with longer-period indicators
Fixed periods may not suit all markets, dynamic optimization could help
Purely technical indicator based, fundamentals may improve accuracy
Inaccurate volume data affects results, need to verify data quality
Insufficient backtesting history, more data needed for validation
Entry points can be improved, rather than direct long on crosses under lowest

Conclusion

In summary, this strategy identifies trends using dual Stochastics and VWMA, which can reliably identify trend reversals in theory. But parameter tuning is needed for specific markets, and false signals risk exists. Recommend combining other factors like fundamentals, long-term trends etc for judgment, to improve strategy Profit Factor. The logic is simple and clear, providing a template for quant trading, which can be modified as needed. It has great application value.

Parameters

Name	Default	What it controls
v_input_1	30	periodK
v_input_2	90	periodK_two
v_input_3	30	VWMA Length

Source Pine Script

/*backtest
start: 2022-10-19 00:00:00
end: 2023-10-25 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
strategy(title="Trend Finder V2", shorttitle="TFV2", format=format.price, precision=2, overlay = true)

//----------Indicator------------//

periodK = input(30)
periodD = 3
smoothK = 2

periodK_two = input(90)
periodD_two = 3
smoothK_two = 2

k = sma(stoch(close, high, low, periodK), smoothK)
d = sma(k, periodD)

k_two = sma(stoch(close, high, low, periodK_two), smoothK_two)
d_two = sma(k, periodD_two)

ts = k + k_two
tsl = vwma(ts, input(30, title = "VWMA Length"))

//--------Label parameter--------// 

up_label = tsl[1] < 100 and tsl > 100 ? 1 : 0
down_label = tsl[1] > 100 and tsl < 100 ? 1 : 0

//----------Color Code-----------//

//tsl_col = tsl > 100 and tsl > tsl[1] ? color.aqua : tsl > 100 and tsl < tsl[1] ? color.green : tsl < 100 and tsl > tsl[1] ? color.maroon : tsl < 100 and tsl < tsl[1] ? color.red : color.silver

//tsl_col = tsl > 100 and ts < 100 and ts > ts[1] ? color.aqua : tsl > 100 and ts > 100 and (ts > ts[1] or ts < ts[1]) ? color.green : tsl < 100 and ts > 100 and ts < ts[1] ? color.red : tsl < 100 and ts < 100 and (ts < ts[1] or ts > ts[1]) ? color.maroon : color.purple  

tsl_col = ts > ts[1] and tsl > tsl[1] ? color.lime : ts < ts[1] and tsl < tsl[1] ? color.red : color.yellow 

ts_col = (tsl_col == color.lime or tsl_col == color.maroon) and (k>k[1] and k < 30) ? color.lime :  (tsl_col == color.green or tsl_col == color.red) and (k < k[1] and k > 70)  ? color.red : color.silver

//-------------Plots-------------//

buy = tsl_col[1] == color.yellow and tsl_col == color.lime ? 1 : 0
sell = tsl_col[1] == color.yellow and tsl_col == color.red ? -1 : 0

plotcandle(open,high,low,close, color=tsl_col)

strategy.entry("Long", strategy.long,when=buy==1)
strategy.close("Long", when=sell==-1)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-10-26. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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