This article explains in detail a quantitative trading strategy that combines both mean reversion and trend following techniques. It aims to trade counter-trend during trending markets and ride the momentum during trending markets.
I. Strategy Logic
The strategy mainly uses Simple Moving Average and RSI indicator to generate trading signals:
When price is below 200-period SMA, it judges the current market as downtrend.
When RSI is below 20, it takes counter-trend mean reversion trades.
When price is above 200-period SMA, it judges the current market as uptrend.
When price crosses above SMA, it takes trend-following trades.
Exits are triggered when RSI exceeds 80 or price drops below SMA by a certain percentage.
Position sizing for mean reversion and trend following can be adjusted separately.
The strategy combines mean reversion and trend following techniques and applies them in different market stages.
II. Advantages of the Strategy
The main advantages are:
Combining two techniques improves strategy adaptiveness.
It can find trading opportunities in trending and ranging markets.
Risks can be controlled by adjusting position sizing.
Simple parameter settings make it easy to implement.
III. Potential Risks
However, the risks are:
Indicators like SMA and RSI are susceptible to false breakouts.
Switching between two modes may lag.
Certain drawdowns need to be endured for long term gains.
IV. Summary
In summary, this article has explained a quantitative strategy utilizing mean reversion and trend following techniques. It can trade in different market stages to improve adaptiveness. But risks like indicator failure and delayed mode switching need to be managed. Overall, it provides a flexible approach to combine different techniques.
/*backtest
start: 2022-09-07 00:00:00
end: 2023-04-05 00:00:00
period: 1d
basePeriod: 1h
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/
// © I11L
//@version=5
strategy("Mean Reversion and Trendfollowing", overlay=true, pyramiding=1, default_qty_value=100000, initial_capital=100000, default_qty_type=strategy.cash, process_orders_on_close=false, calc_on_every_tick=false)
// Input for the starting date
start_date = input(timestamp("1 Feb 2000 12:00"), title="Starting Date")
enableMeanReversion = input.bool(true)
enableTrendfollowing = input.bool(true)
trendPositionFactor = input.float(1)
meanReversionPositionFactor = input.float(0.5)
// Convert the input string to a timestamp
start_ts = timestamp(year(start_date), month(start_date), dayofmonth(start_date), 0, 0)
// Check if the current bar's time is greater than or equal to the start timestamp
start_condition = time >= start_ts
var tradeOrigin = ""
sma200 = ta.sma(close,200)
rsi2 = ta.rsi(close,2)
isMeanReversionMode = close < sma200 or not(enableTrendfollowing)
isTrendfollowingMode = close > sma200 or not(enableMeanReversion)
isRsiBuy = rsi2 < 20 and enableMeanReversion
isRsiClose = rsi2 > 80 and enableMeanReversion
isSmaBuy = close > sma200 and enableTrendfollowing
isSmaClose = close < sma200 * 0.95 and enableTrendfollowing
isBuy = (isMeanReversionMode and isRsiBuy) or (isTrendfollowingMode and isSmaBuy)
positionSizeFactor = isSmaBuy ? trendPositionFactor : meanReversionPositionFactor
// Only execute the strategy after the starting date
if (start_condition)
if (isBuy and strategy.opentrades == 0)
tradeOrigin := isSmaBuy ? "SMA" : "RSI"
strategy.entry("My Long Entry Id", strategy.long, qty=(strategy.equity / close) * positionSizeFactor, comment=str.tostring(positionSizeFactor))
isClose = tradeOrigin == "SMA" ? isSmaClose : isRsiClose
if (isClose)
strategy.exit("Exit", limit = close)
plot(sma200)
plot(sma200 * 0.95, color=color.orange)