Williams %R Crossover Strategy with Moving Average Filter

Author: ChaoZhang, Date: 2023-09-19 21:57:46


This strategy utilizes the Williams %R crossover signals and adds a moving average filter to create a flexible short-term trading system. It can capture overbought and oversold situations clearly, while the MA filter avoids market noise for higher stability.

Strategy Logic

  1. Calculate Williams %R and 200-period simple moving average (MA).

  2. Go long when %R crosses above -50 level by a threshold and close is above MA.

  3. Go short when %R crosses below -50 level by a threshold and close is below MA.

  4. If long, close position when close reaches take profit (entry price + take profit pips) or stop loss level (entry price - stop loss pips).

  5. If short, close position when close reaches take profit (entry price - take profit pips) or stop loss level (entry price + stop loss pips).

The strategy capitalizes on the overbought/oversold nature of Williams %R, and combines MA trend filter for more reliable signals. The stop loss/take profit logic is simple and clear. Overall it is a complete short-term system.

Advantage Analysis

  1. Williams %R effectively identifies overbought/oversold levels with clear signals.

  2. MA filter adds trend bias to avoid whipsaws.

  3. Customizable parameters for flexibility.

  4. Trailing stop loss locks in most profits.

  5. Simple clear logic, easy to understand and implement. Good for short-term trading.

  6. Applicable to multiple products with flexibility.

Risk Analysis

  1. Williams %R has lagging effect, may miss some opportunities.

  2. MA filter also has some lag.

  3. Strict overbought/oversold rules may miss some trends.

  4. Stop loss too tight may be stopped out by whipsaws.

  5. Stop loss too wide may limit profits.

  6. Parameters need tuning for different market environments.

Optimization Directions

  1. Optimize parameters for higher win rate.

  2. Add other filters like MACD, KDJ etc.

  3. Try different MA types like exponential MA.

  4. Add trend bias, only trade in trend direction.

  5. Optimize stop loss strategies like dynamic stops, chandelier exits etc.

  6. Try position sizing like fixed fractional, Martingale etc.

  7. Utilize machine learning for better parameter optimization.


This strategy combines Williams %R overbought/oversold signals with MA trend filter into a simple short-term system. It has clear entry signals and stop loss/take profit logic. Further improvements can be made through parameter tuning, indicator selection, stop loss management etc for more robustness. Easy to implement and expand on, this strategy is well suited for short-term traders.

start: 2023-08-19 00:00:00
end: 2023-09-18 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]

strategy("Williams %R Cross Strategy with MA Filter", overlay=true)

// User Inputs
wrLength = input(14, title="Williams %R Length")
crossPips = input(10, title="Cross Threshold (Pips)")
takeProfitPips = input(30, title="Take Profit (Pips)")
stopLossPips = input(20, title="Stop Loss (Pips)")

// Calculate Williams %R
wrHigh = ta.highest(high, wrLength)
wrLow = ta.lowest(low, wrLength)
wr = (wrHigh - close) / (wrHigh - wrLow) * -100

// Calculate 200-period Simple Moving Average
ma200 = ta.sma(close, 200)

// Entry Conditions
enterLong = ta.crossover(wr, -50 - crossPips) and close > ma200
enterShort = ta.crossunder(wr, -50 + crossPips) and close < ma200

// Exit Conditions
exitLong = close >= (strategy.position_avg_price + (takeProfitPips / syminfo.mintick)) or close <= (strategy.position_avg_price - (stopLossPips / syminfo.mintick))
exitShort = close <= (strategy.position_avg_price - (takeProfitPips / syminfo.mintick)) or close >= (strategy.position_avg_price + (stopLossPips / syminfo.mintick))

// Order Management
if enterLong
    strategy.entry("Long", strategy.long)
if enterShort
    strategy.entry("Short", strategy.short)

if exitLong

if exitShort