Bitcoin Volatility Band Reversion Strategy

Author: ChaoZhang, Date: 2023-10-12 17:38:39


This strategy is a pullback system designed for securities with high volatility, so naturally Bitcoin is an excellent choice for trading this. This could be used both on a daily chart or on lower timeframes (I found good results on 3hr timeframe but haven’t tested it on anything under 1hr).

Strategy Logic

The strategy calculates volatility by comparing the change in closing price of the previous 2 candles, and uses this change in price to generate a moving average. A band is wrapped around the moving average with a standard deviation of 1 for the inner band and 2 for the outer band. If the price is above a pre-set MA (moving average) filter then it is determined we are in an uptrend so the strategy will issue a buy signal when we are in an uptrend and there is a pullback which causes the lower inner deviation band to be spiked, but if the price continues and falls through the outer deviation band then a buy signal will not issue as this detriments that the volatility spike is to great. You can see a spike “buy” event occur on the indicator where the background is coloured green. For a short/sell then there will be a spike on the upper inner band and we are below the pre-set MA filter, for this it shows with red background on the indicator.

The user can change the date range they wish to test, the moving average period for the volatility tracking and the inner and outer band deviations. On BTC I left the inner deviation and outer deviation bands on standard settings but found the 3 period volatility tracking to be good for trading 1 day chart and the 5 period volatility tracking good for the 3hr chart. Since this is not a buy and hold strategy then for trading you would probably want to stick with the most liquid coins so you can get in and out very fast on any exchange. If you wanted to tray this on less volatile markets then changing the inner deviation band to ~0.75 would work okay in various futures markets likely stocks as well. The take profit and stop loss levels are based on a multiple of the trading range looking back the past 7 candles.

Advantages of the Strategy

  • Utilizes volatility trading to capture market turning points
  • Trades both long and short, profiting in up and down markets
  • Simple standard parameter settings easy to use
  • Parameters can be easily optimized for different underlyings
  • Reasonable stop loss and take profit settings help lock in profits

Risks of the Strategy

  • High volatility underlyings risk larger losses
  • Frequent long/short switching incurs higher trading costs
  • Short-term operations require close market monitoring
  • Difficult to stop loss when low market liquidity
  • Poor parameter tuning can lead to over-trading

Risk Mitigation Methods:

  1. Choose appropriate volatile underlyings, control position sizing.

  2. Optimize parameters to reduce ineffective trades.

  3. Use stop loss and take profit, strict money management.

  4. Focus on execution efficiency, choose liquid underlyings.

  5. Adjust parameters to suit different underlying characteristics.

Optimization Directions

The strategy can be optimized in the following aspects:

  1. Optimize moving average period to better track volatility of different underlyings.

  2. Adjust volatility band parameters to better fit specific underlying’s volatility range.

  3. Add other filters like volume spike to further validate signals.

  4. Use machine learning techniques to dynamically optimize parameters for adaptiveness.

  5. Test on higher frequency timeframes to capture more trading opportunities.

  6. Add moving stop loss/take profit tracking to lock in profits more.

  7. Combine with other indicators or models to build quantitative portfolio strategies.


The strategy overall is rather simple and intuitive, identifying reversals via volatility indicator to capture market turning points. There is large optimization space by adjusting parameters and incorporating other technical indicators to further enhance stability and profitability. However traders need to be aware of overfitting and curve fitting problems. This strategy suits short-term trading more, requiring strict money management to control risks. If mastered properly, it can become a powerful tool for trading high volatility cryptocurrencies.

start: 2023-09-11 00:00:00
end: 2023-10-11 00:00:00
period: 4h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]

// This source code is subject to the terms of the Mozilla Public License 2.0 at
// © gary_trades
//This script is designed to be used on volatile securities/tickers so is best suited for day charts on Crypto (particularly good for BTC).
//It takes both long and short trades and the main indicator settings can be changed by the use so they can test for ideal settings for ticker of interest.


strategy("BTC Volatility Band Strategy", shorttitle="Vol Band Strategy", overlay=false, margin_long=100, margin_short=100)

CandleChange = ((close - close[1])/close)*100         //OR CandleChange = ((close[2] - close[1])/close)*100
plot(CandleChange,, linewidth = 1)

MAlen = input(7, minval=3, maxval=30, title=" MA Length")
MAout = sma(CandleChange, MAlen)
plot(MAout,, display=display.none)

InnerBand = input(1.0, minval=0.5, maxval=5, title="Inner Band")
OuterBand = input(2.00, minval=0.5, maxval=10, title="Outer Band")
devInner = InnerBand * stdev(CandleChange, MAlen)
devOuter = OuterBand * stdev(CandleChange, MAlen)

upper1 = MAout + devInner
lower1 = MAout - devInner
b1 = plot(upper1, "Upper Inner", color=color.gray)
b2 = plot(lower1, "Lower Inner", color=color.gray)
upper2 = MAout + devOuter
lower2 = MAout - devOuter
b3 = plot(upper2, "Upper Outer", color=color.gray)
b4 = plot(lower2, "Lower Outer", color=color.gray)
fill(b1, b3, color.rgb(250,145,175,70), title="Background")
fill(b2, b4, color.rgb(250,145,175,70), title="Background")

band1 = hline(25, "Upper Band", color=color.gray, linestyle=hline.style_dotted, linewidth=2)
band0 = hline(-25, "Lower Band", color=color.gray, linestyle=hline.style_dotted, linewidth=2)

VolFilterL = CandleChange <= lower1 and CandleChange > lower2
SMAFilterL = close[1] > sma(close[1], 50)
PriceFilterL = close > lowest(close,7)
LongFilter = VolFilterL and SMAFilterL and PriceFilterL
bgcolor(LongFilter ?, 80) : na)

VolFilterS = CandleChange >= upper1 and CandleChange < upper2
SMAFilterS = close[1] < sma(close[1], 50)
PriceFilterS = close < highest(close,7)
ShortFilter = VolFilterS and SMAFilterS and PriceFilterS
bgcolor(ShortFilter ?, 80) : na)

fromDay = input(defval = 1, title = "From Day", minval = 1, maxval = 31)
fromMonth = input(defval = 1, title = "From Month", minval = 1, maxval = 12)
fromYear = input(defval = 2000, title = "From Year", minval = 1970)
toDay = input(defval = 1, title = "To Day", minval = 1, maxval = 31)
toMonth = input(defval = 1, title = "To Month", minval = 1, maxval = 12)
toYear = input(defval = 2100, title = "To Year", minval = 1970)

startDate = timestamp("America/New_York", fromYear, fromMonth, fromDay, 00, 00)
finishDate = timestamp("America/New_York", toYear, toMonth, toDay, 00, 00)
time_condition = time >= startDate and time <= finishDate

Risk = (high[7] - low[7])/ 7
Profit = Risk*1.15
Loss = Risk*0.65

AlertMSG = "New stategy position" + tostring(strategy.position_size)

if (time_condition) 
    strategy.entry("Long", strategy.long, when = LongFilter, alert_message=AlertMSG)
    if (LongFilter)
        LongStop = strategy.position_avg_price - Loss
        LongProfit = strategy.position_avg_price + Profit 
        strategy.exit("TP/SL", "Long", stop=LongStop, limit=LongProfit)

if (time_condition)
    strategy.entry("Short", strategy.short, when = ShortFilter, alert_message=AlertMSG)
    if (ShortFilter)
        ShortStop = strategy.position_avg_price + Loss
        ShortProfit = strategy.position_avg_price - Profit 
        strategy.exit("TP/SL", "Short", stop=ShortStop, limit=ShortProfit)