Optimization

The Optimization Settings panel provides control over how each analytical subsystem — Market Waves, Core Toolkit, and Nautilus System — interprets market data.

These parameters directly affect signal generation accuracy, detection sensitivity, and backtest realism. Not all condition types include optimization inputs; only the components that rely on configurable sensitivity, timeframe, or detection logic provide adjustable parameters.

MARKET WAVES SETTINGS

These inputs refine the reaction speed and behavior of the Market Waves engine, affecting how it detects short- and long-term trend dynamics.

• Trend Signals (Sensitivity) Adjusts how reactive the trend detection algorithm is.

  • Lower values = higher sensitivity, faster reaction to small swings.

  • Higher values = smoother, long-term trend interpretation. Ideal for tuning trend-following vs. mean-reversion responsiveness.

• FlowTrend (Timeframe Source) Defines the timeframe used for the higher-timeframe market flow calculation.

  • Chart — uses the current chart’s timeframe.

  Selecting a lower timeframe may cause repainting. For best accuracy, use a higher timeframe (e.g., 1H flow on a 15M chart) to maintain realistic, non-repainting signal behavior.

• Candlestick Patterns Chooses which candlestick setups are considered valid triggers for pattern-based conditions. Options include All, Hammer, Engulfing, Doji, Star, Harami, Tweezer Top/Bottom, etc.

MARKET CORE SETTINGS

These parameters control the precision and complexity of market structure and price-action components.

Market Structure – Length

Defines the swing lookback used to identify structural highs and lows. Longer lengths reduce noise but may delay structure changes.

Order Blocks

• Use Last: Specifies how many recent Order Blocks are considered in logic.

• Mitigation Method: Determines whether touch, wick, close, or average contact qualifies as mitigation.

• Macro Blocks: Enables higher-timeframe (macro) block detection for institutional context.

Fair Value Gaps (FVG)

• Use Last: Limits the number of recent gaps used in detection logic.

• PoC: Enables or disables the Point of Control line inside each gap.

• PoC Mode: Choose between Mean or Highest Volume calculation.

• FVG Threshold: Sets the minimum required size (in ticks or %) for a valid gap.

Swing Failure Pattern (SFP)

• Length: Defines how many bars are used to confirm local swing highs/lows.

• Threshold: Sets the minimum volume or strength required for SFP validation. These filters ensure only significant liquidity sweeps are recognized.

Support / Resistance

• Sensitivity: Controls how tightly the algorithm reacts to local pivot levels.

• Strength: Determines how many price interactions validate a level as strong support/resistance.

Channels / Wedges

• Channels: Sets the detection range (Small → Macro) for parallel channel formation.

• Wedges: Sets the detection range (Small → Macro) for parallel channel formation.

Liquidity Grab

• Length: Specifies how many bars are evaluated to detect liquidity sweeps above highs or below lows.

Session

• UTC Offset: Adjusts the session time zone relative to UTC.

• Session Range: Defines the intraday session window (e.g., 08:00–08:45) used for breakout analysis.

NAUTILUS SETTINGS

The Nautilus System offers preset profiles optimized for different trading styles, balancing reactivity and smoothness of oscillator-based signals.

• Trader Preset Select between Scalper, Day Trader, and Swing Trader profiles:

  • Scalper: Highest reactivity, fast signal turnover for lower timeframes.

  • Day Trader: Balanced responsiveness, suitable for intraday directional plays.

  • Swing Trader: Smooth long-cycle detection, ideal for larger market phases.

Each preset automatically adjusts core Nautilus parameters such as oscillator length, peak detection sensitivity, and divergence threshold.

The Optimization Settings define how deeply the BigBeluga Backtester analyzes market structure, trend, and momentum. By fine-tuning these parameters, traders can replicate any market condition — from volatile scalping environments to multi-day trend phases — and ensure every test reflects realistic system behavior.

These inputs bridge discretion and data: turning high-level concepts into precisely adjustable, backtestable models that evolve with market dynamics.