Strategy vault / Mean reversion
Long Term Quantitative Strategy Based on Volatility Bands Reversal
This article explains in detail a long-term quantitative trading strategy using volatility bands to identify reversals. It takes long positions when prices break through the lower band to ride the upside move. I…
This article explains in detail a long-term quantitative trading strategy using volatility bands to identify reversals. It takes long positions when prices break through the lower band to ride the upside move.
I. Strategy Logic
The core indicator is volatility bands, calculated as:
Compute middle, upper and lower moving average bands.
A buy signal is generated when price breaks up through the lower band.
A sell signal is generated when price breaks the upper band.
Exits can be on sell signals or upper band breaks.
Stop loss is a fixed percentage.
This allows buying into downward phases, then exiting via profit taking or stops to capitalize on reversals.
II. Advantages of the Strategy
The biggest advantage is using volatility bands to identify reversal points, a mature technical analysis technique.
Another advantage is the stop loss mechanism to control risk per trade.
Finally, pyramiding also helps phase in profits after reversals.
III. Potential Risks
However, some potential issues exist:
Firstly, moving averages have lag and may cause missed best entry timing.
Secondly, profit taking and stop loss levels require careful optimization.
Finally, long holding periods mean enduring certain drawdowns.
IV. Summary
In summary, this article has explained a long-term quantitative trading strategy using volatility bands to capitalize on reversals. It can effectively detect reversal opportunities for long-term holdings. But risks like MA lags need prevention, and optimization is required for exits. Overall it provides a robust long-term trading approach.
Parameters
| Name | Default | What it controls |
|---|---|---|
| v_input_1 | 8 | Band Average |
| v_input_2 | 13 | Volatility Period |
| v_input_3 | 3.33 | Deviation Factor |
| v_input_4 | 0.9 | Lower Band Adjustment |
| v_input_5 | 10 | Risk % of capital |
| v_input_6 | 6 | Stop Loss |
| v_input_7 | 0 | Exit on: touch_upperband | Sell_Signal |
Source Pine Script
/*backtest
start: 2023-09-07 00:00:00
end: 2023-09-12 04:00:00
period: 14m
basePeriod: 1m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © ediks123
//strategy logic has been borrowed from ceyhun and tweaked the settings for back testing
//@version=4
//SPY 4 hrs settings 8, 13 , 3.33 , 0.9 on 4 hrs chart
//QQQ above settings is good , but 13, 13 has less number of bars
//QQQ 4 hrs settings 13, 13 , 3.33 , 0.9 on 4 hrs chart
strategy(title="Volatility Bands Reversal Strategy", shorttitle="VolatilityBandReversal" , overlay=true, pyramiding=2, default_qty_type=strategy.percent_of_equity, default_qty_value=20, initial_capital=10000, currency=currency.USD) //default_qty_value=10, default_qty_type=strategy.fixed,
av = input(8, title="Band Average")
vp = input(13, title="Volatility Period")
df = input(3.33,title="Deviation Factor",minval=0.1)
lba = input(0.9,title="Lower Band Adjustment",minval=0.1)
riskCapital = input(title="Risk % of capital", defval=10, minval=1)
stopLoss=input(6,title="Stop Loss",minval=1)
exitOn=input(title="Exit on", defval="touch_upperband", options=["Sell_Signal", "touch_upperband"])
src = hlc3
typical = src >= src[1] ? src - low[1] : src[1] - low
deviation = sum( typical , vp )/ vp * df
devHigh = ema(deviation, av)
devLow = lba * devHigh
medianAvg = ema(src, av)
emaMediaAvg=ema(medianAvg, av)
upperBandVal= emaMediaAvg + devHigh
lowerbandVal= emaMediaAvg - devLow
MidLineVal=sma(medianAvg, av)
UpperBand = plot ( upperBandVal, color=#EE82EE, linewidth=2, title="UpperBand")
LowerBand = plot ( lowerbandVal , color=#EE82EE, linewidth=2, title="LowerBand")
MidLine = plot (MidLineVal, color=color.blue, linewidth=2, title="MidLine")
buyLine = plot ( (lowerbandVal + MidLineVal )/2 , color=color.blue, title="BuyLine")
up=ema(medianAvg, av) + devHigh
down=ema(medianAvg, av) - devLow
ema50=ema(hlc3,50)
plot ( ema50, color=color.orange, linewidth=2, title="ema 50")
//outer deviation
//deviation1 = sum( typical , vp )/ vp * 4
//devHigh1 = ema(deviation, av)
//devLow1 = lba * devHigh
//medianAvg1 = ema(src, av)
//UpperBand1 = plot (emaMediaAvg + devHigh1, color=color.red, linewidth=3, title="UpperBand1")
//LowerBand1 = plot (emaMediaAvg - devLow1, color=color.red, linewidth=3, title="LowerBand1")
//
///Entry Rules
//1)First candle close below the Lower Band of the volatility Band
//2)Second candle close above the lower band
//3)Third Candle closes above previous candle
Buy = close[2] < down[2] and close[1]>down[1] and close>close[1]
//plotshape(Buy,color=color.blue,style=shape.arrowup,location=location.belowbar, text="Buy")
//barcolor(close[2] < down[2] and close[1]>down[1] and close>close[1] ? color.blue :na )
//bgcolor(close[2] < down[2] and close[1]>down[1] and close>close[1] ? color.green :na )
///Exit Rules
//1)One can have a static stops initially followed by an trailing stop based on the risk the people are willing to take
//2)One can exit with human based decisions or predefined target exits. Choice of deciding the stop loss and profit targets are left to the readers.
Sell = close[2] > up[2] and close[1]<up[1] and close<close[1]
//plotshape(Sell,color=color.red,style=shape.arrowup,text="Sell")
barcolor(close[2] > up[2] and close[1]<up[1] and close<close[1] ? color.yellow :na )
bgcolor(close[2] > up[2] and close[1]<up[1] and close<close[1] ? color.red :na )
//Buyer = crossover(close,Buy)
//Seller = crossunder(close,Sell)
//alertcondition(Buyer, title="Buy Signal", message="Buy")
//alertcondition(Seller, title="Sell Signal", message="Sell")
//Entry--
//Echeck how many units can be purchased based on risk manage ment and stop loss
qty1 = (strategy.equity * riskCapital / 100 ) / (close*stopLoss/100)
//check if cash is sufficient to buy qty1 , if capital not available use the available capital only
qty1:= (qty1 * close >= strategy.equity ) ? (strategy.equity / close) : qty1
strategy.entry(id="vbLE", long=true, qty=qty1, when=Buy)
bgcolor(strategy.position_size>=1 ? color.blue : na)
// stop loss exit
stopLossVal = strategy.position_size>=1 ? strategy.position_avg_price * ( 1 - (stopLoss/100) ) : 0.00
//draw initil stop loss
plot(strategy.position_size>=1 ? stopLossVal : na, color = color.purple , style=plot.style_linebr, linewidth = 2, title = "stop loss") //, trackprice=true)
strategy.close(id="vbLE", comment="SL exit Loss is "+tostring(close - strategy.position_avg_price, "###.##") , when=abs(strategy.position_size)>=1 and close < stopLossVal )
//close on Sell_Signal
strategy.close(id="vbLE", comment="Profit is : "+tostring(close - strategy.position_avg_price, "###.##") , when=strategy.position_size>=1 and exitOn=="Sell_Signal" and Sell)
//close on touch_upperband
strategy.close(id="vbLE", comment="Profit is : "+tostring(close - strategy.position_avg_price, "###.##") , when=strategy.position_size>=1 and exitOn=="touch_upperband" and (crossover(close, up) or crossover(high, up)))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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