StockSharp.Strategies.0335_VWAP_Hidden_Markov_Model.py

VWAP Hidden Markov Model (Python Version)

The VWAP Hidden Markov Model strategy is built around that trades based on VWAP with Hidden Markov Model for market state detection.

Testing indicates an average annual return of about 100%. It performs best in the forex market.

Signals trigger when Markov confirms trend changes on intraday (5m) data. This makes the method suitable for active traders.

Stops rely on ATR multiples and factors like HmmDataLength, StopLossPercent. Adjust these defaults to balance risk and reward.

Details

• Entry Criteria: see implementation for indicator conditions.
• Long/Short: Both directions.
• Exit Criteria: opposite signal or stop logic.
• Stops: Yes, using indicator-based calculations.
• Default Values:
  • HmmDataLength = 100
  • StopLossPercent = 2m
  • CandleType = TimeSpan.FromMinutes(5).TimeFrame()
• Filters:
  • Category: Trend following
  • Direction: Both
  • Indicators: Markov
  • Stops: Yes
  • Complexity: Intermediate
  • Timeframe: Intraday (5m)
  • Seasonality: No
  • Neural Networks: Yes
  • Divergence: No
  • Risk Level: Medium