VWAP Williams R Strategy (C#)

by StockSharp

VWAP Williams R Strategy The VWAP Williams %R strategy focuses on intraday reversion around the Volume Weighted Average Price. It observes when price drifts away from VWAP while the Williams %R oscill...

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NuGet 5.0.2 Install-Package StockSharp.Strategies.0201_VWAP_Williams_R -Version 5.0.2
VWAP Williams R Strategy (C#)

VWAP Williams R Strategy

The VWAP Williams %R strategy focuses on intraday reversion around the Volume Weighted Average Price. It observes when price drifts away from VWAP while the Williams %R oscillator reaches oversold or overbought territory. The assumption is that extreme readings near VWAP often lead to a snapback toward the mean.

Testing indicates an average annual return of about 40%. It performs best in the crypto market.

When the oscillator drops below -80 and price trades under VWAP, the setup implies selling pressure is fading and a rebound may follow. Conversely, a reading above -20 while price is positioned above VWAP warns that buyers are exhausted and a pullback is likely. The strategy opens trades in the direction of a potential return to VWAP and watches for that move to complete.

This approach fits active intraday traders who prefer frequent mean reversion opportunities. A small stop‑loss relative to VWAP keeps risk contained while still allowing enough room for price to fluctuate before reversing.

Details

  • Entry Criteria:
    • Long: Price < VWAP && Williams %R < -80 (oversold below VWAP)
    • Short: Price > VWAP && Williams %R > -20 (overbought above VWAP)
  • Long/Short: Both sides.
  • Exit Criteria:
    • Long: Exit long position when price breaks above VWAP
    • Short: Exit short position when price breaks below VWAP
  • Stops: Yes.
  • Default Values:
    • WilliamsRPeriod = 14
    • StopLossPercent = 2m
    • CandleType = TimeSpan.FromMinutes(5)
  • Filters:
    • Category: Mixed
    • Direction: Both
    • Indicators: VWAP Williams R
    • Stops: Yes
    • Complexity: Intermediate
    • Timeframe: Intraday
    • Seasonality: No
    • Neural networks: No
    • Divergence: No
    • Risk Level: Medium

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