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Strategy vault / Trend following

SuperTrend Trend Following Strategy

This strategy uses the SuperTrend indicator to determine price trend direction and generate trading signals, belonging to the trend following strategy category. It is specifically tested on Tesla (TSLA) 1-minute…

Pine ScriptTrend followingMachine learningATRSupertrend
Author
ChaoZhang
Complexity
Starter · 45 lines
Parameters
2
Backtest bar
1h · intraday
Tested on
BTC/USDT · futures
Jump to codeView in vaultOriginal on FMZ

Overview

This strategy uses the SuperTrend indicator to determine price trend direction and generate trading signals, belonging to the trend following strategy category. It is specifically tested on Tesla (TSLA) 1-minute chart with decent results.

Strategy Logic

  1. Calculate ATR and average of highest high and lowest low to determine SuperTrend upper and lower bands based on multiplier.

  2. Determine if price breaks above upper band or below lower band to identify SuperTrend direction.

  3. Long signal when price crosses above lower band. Short signal when price crosses below upper band.

  4. Can choose to enter on next bar's open when signal is triggered, or immediately when price hits SuperTrend bands.

Advantages

  1. SuperTrend clearly identifies trends, easy to program.

  2. Flexible entry options suit different trader preferences.

  3. Can quickly capture medium-term trends, suitable for trend following.

  4. Frequent trading allows expansions and enhancements.

Risks

  1. SuperTrend lags potentially missing best entries.

  2. High trading frequency leads to larger slippage costs.

  3. No risk control tools like stop loss.

  4. Backtest solely on Tesla 1-min data, hard to prove strategy validity.

Possible Solutions:

  1. Adjust parameters to reduce lag.

  2. Add slippage control to limit costs.

  3. Incorporate stop loss to control loss per trade.

  4. Backtest across more products and timeframes for robustness.

Optimization Directions

  1. Test different parameter sets to reduce lag.

  2. Add filters to avoid whipsaws.

  3. Optimize money management for higher efficiency.

  4. Incorporate machine learning to predict SuperTrend direction.

  5. Add other indicators to verify signals and improve stability.

Summary

This strategy uses SuperTrend to identify medium-term trend direction for trading signals, typical of trend following strategies. The overall framework is simple and effective, but can be further improved in areas like entry opportunities, risk management, parameter selection etc. With more historical data across products and integrated techniques like machine learning, it can be significantly enhanced in stability and profitability.

Parameters

NameDefaultWhat it controls
v_input_13SuperTrend Multiplier
v_input_2120SuperTrend Period

Source Pine Script

/*backtest
start: 2023-08-24 00:00:00
end: 2023-09-23 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
strategy("QuantNomad - SuperTrend - TSLA - 1m", overlay = true, default_qty_type = strategy.percent_of_equity, default_qty_value = 100)

// INPUTS //
st_mult   = input(3,   title = 'SuperTrend Multiplier', minval = 0, maxval = 100, step = 0.01)
st_period = input(120, title = 'SuperTrend Period',     minval = 1)

// CALCULATIONS //
up_lev = hl2 - (st_mult * atr(st_period))
dn_lev = hl2 + (st_mult * atr(st_period))

up_trend   = 0.0
up_trend   := close[1] > up_trend[1]   ? max(up_lev, up_trend[1])   : up_lev

down_trend = 0.0
down_trend := close[1] < down_trend[1] ? min(dn_lev, down_trend[1]) : dn_lev

// Calculate trend var
trend = 0
trend := close > down_trend[1] ? 1: close < up_trend[1] ? -1 : nz(trend[1], 1)

// Calculate SuperTrend Line
st_line = trend ==1 ? up_trend : down_trend

// Plotting
plot(st_line, color = trend == 1 ? color.green : color.red , style = plot.style_line, linewidth = 2, title = "SuperTrend")

plotshape(crossover( close, st_line), location = location.belowbar, color = color.green)
plotshape(crossunder(close, st_line), location = location.abovebar, color = color.red)

// Strategy with "when"
//strategy.entry("long",  true,  when = crossover( close, down_trend[1]))
//strategy.entry("short", false, when = crossunder(close, up_trend[1]))

// Strategy with stop orders
strategy.entry("long",  true,  stop = down_trend[1])
strategy.entry("short", false, stop = up_trend[1])

Published by ChaoZhang on the FMZ strategy square · last modified 2023-09-24. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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