Strategy vault / Trend following
Trend Following Strategy Based on Dynamic Support and Resistance
This article explains in detail a trend following strategy that utilizes dynamic support and resistance levels. It forms upper and lower bands using moving averages and ATR to track trends. I. Strategy Logic The main…
This article explains in detail a trend following strategy that utilizes dynamic support and resistance levels. It forms upper and lower bands using moving averages and ATR to track trends.
I. Strategy Logic
The main indicators and logic include:
Calculating highest high moving average over a period as the upper band.
Using ATR to compute buffer distance for stop loss.
Upper band minus buffer sets the lower band.
Take long when price breaks above upper band; exit when price breaks below lower band.
The upper and lower bands construct dynamic support and resistance zones. By trend riding breakouts and quick stops, trading risks can be controlled.
II. Advantages of the Strategy
The main advantages are:
Dynamic bands can capture trend opportunities.
ATR stop loss sets stops based on market volatility.
Larger profit target than stop loss benefits profits.
Simple rules make it easy to implement.
III. Potential Risks
However, some potential issues exist:
Moving averages and ATR have lagging issues.
Larger drawdowns need to be endured.
No limit on number of entries.
Parameters need optimization for different products.
IV. Summary
In summary, this article has explained a trend following strategy using moving averages and ATR to form dynamic bands. It can set stop loss and take profit based on volatility to ride trends. But risks like indicator lag and drawdown control need attention. Overall it provides a simple trend tracking method.
Parameters
| Name | Default | What it controls |
|---|---|---|
| v_input_1 | 200 | Highest Length |
| v_input_2 | 10 | Highest Average |
| v_input_3 | 14 | ATR Length |
| v_input_4 | 2 | ATR Multiplier |
Source Pine Script
/*backtest
start: 2023-01-01 00:00:00
end: 2023-09-14 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
//@version=4
strategy("I Like Winners And Hate Loosers!", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=100)
highest_length = input(200, type=input.integer, minval=1, title="Highest Length")
highest_average = input(10, type=input.integer, minval=1, title="Highest Average")
atr_length = input(14, type=input.integer, minval=1, title="ATR Length")
atr_multiplier = input(2, type=input.integer, minval=1, title="ATR Multiplier")
a = atr(atr_length) * atr_multiplier
h = sma(highest(high, highest_length), highest_average)
l = h - a
buy_signal = crossover(close, h)
sell_signal = crossunder(close, l)
strategy.entry("Buy", strategy.long, when=buy_signal)
strategy.close("Buy", when=sell_signal)
plot(h, title="H", color=color.green, transp=50, linewidth=2)
plot(l, title="L", color=color.red, transp=50, linewidth=2)
Published by ChaoZhang on the FMZ strategy square · last modified 2023-09-15. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.
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