Trend Deviation Index with Moving Average Strategy

This strategy calculates the Trend Deviation Index (TSI) of price, processes TSI with moving average, and forms the moving average line of TSI. Combined with the price candlestick direction, it determines whether the…

Pine ScriptTrend following Machine learning Breakout

Author: ChaoZhang
Complexity: Starter · 45 lines
Parameters: 6
Backtest bar: 30m · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Principles

The main steps of this strategy are:

Calculate the price change percentage pct
Double smooth pct with HMA to get double_smoothed_pc
Calculate double HMA of absolute pct to get double_smoothed_abs_pc
Calculate TSI value: (100 * (double_smoothed_pc / double_smoothed_abs_pc))
Process TSI value with HMA to get TSI moving average line tsihmaline
Compare TSI value and TSI moving average line, above moving average is uptrend, below is downtrend
In uptrend, if price is also rising, generate buy signal
In downtrend, if price is also falling, generate sell signal

Through above steps, it determines the overall trend direction, combined with actual price movement, to generate trading signals.

Advantages

Double HMA smoothing filters out short-term noise and locks in major trend
TSI and its moving average line determine overall trend direction
Combined with price candlestick avoids false breakout, improves signal reliability
Customizable parameters adapt to different cycle markets
Graphical visualization, green for uptrend, red for downtrend

Risks

May generate multiple false signals during range-bound market
Moving average line lags at turning points, possibly missing best entry point
Frequent parameter tuning needed to adapt to changing market
Based on single TSI indicator, can be optimized with other indicators

Enhancement

Add filters to avoid false signals during consolidation
Add other indicators to confirm trend reversal points
Auto-optimize parameters via machine learning etc
Introduce stop loss to control single trade loss

Conclusion

This strategy uses TSI to determine trend direction combined with price candlesticks to generate trading signals, which can effectively catch the trend, buying in uptrend and selling in downtrend. But there are also risks, requiring optimization to improve stability. Overall, this strategy is intuitive and easy to understand, suitable for traders familiar with technical indicators.

Parameters

Name	Default	What it controls
v_input_1	50	Long Length
v_input_2	50	Short Length
v_input_3	7	Signal Length
v_input_4_open	0	Source: open \| high \| low \| close \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_5	250	Upper Line
v_input_6	-250	Lower Line

Source Pine Script

/*backtest
start: 2023-10-29 00:00:00
end: 2023-11-05 00:00:00
period: 30m
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
strategy(title="TSIHULLBOT", shorttitle="TSICCIHULL", default_qty_type=strategy.percent_of_equity, default_qty_value=100)
long = input(title="Long Length", type=input.integer, defval=50)
short = input(title="Short Length", type=input.integer, defval=50)
signal = input(title="Signal Length", type=input.integer, defval=7)
price = input(title="Source",type=input.source,defval=open)
lineupper = input(title="Upper Line", type=input.integer, defval=250)
linelower = input(title="Lower Line", type=input.integer, defval=-250)
double_smooth(price, long, short) =>
    fist_smooth = hma(price, long)
    hma(fist_smooth, short)
pc = change(price)
double_smoothed_pc = double_smooth(pc, long, short)
double_smoothed_abs_pc = double_smooth(abs(pc), long, short)
tsi_value = (100 * (double_smoothed_pc / double_smoothed_abs_pc))*5
tsihmaline=(hma(tsi_value,signal))*5
clr = tsihmaline < tsi_value ? color.red : color.lime
clr2 = tsi_value < tsi_value[1] ? color.red : color.lime
i1=plot(lineupper+3, color=color.black, linewidth=3)
i2=plot(linelower+3, color=color.black, linewidth=3)
i3=plot(lineupper, color=clr)
i4=plot(linelower, color=clr)
trendv=tsihmaline/5.6
plot(trendv, linewidth=7,  color=color.black)
plot(trendv, linewidth=4,  color=color.yellow)
j1=plot(tsi_value, linewidth=5, color=color.black)
j2=plot(tsi_value[1], linewidth=5, color=color.black)
j3=plot(tsi_value, color=clr2)
j4=plot(tsi_value[1], color=clr2)
fill(i3,i4,color=clr,transp=90)
fill(j3,j4,color=clr2,transp=15)
longCondition = tsihmaline>tsihmaline[1] and price>price[1]
if (longCondition)
    strategy.entry("Buy ⤴️", strategy.long)
shortCondition = tsihmaline<tsihmaline[1] and price<price[1]
if (shortCondition)
    strategy.entry("Sell ⤵️", strategy.short)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-11-06. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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