trading-gates-pattern-filter

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Pattern filter normalization and multi-gate order entry system

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[Finance Operations]

When & Why to Use This Skill

This Claude skill provides a sophisticated framework for algorithmic trading, featuring normalized pattern filtering and a multi-gate order entry system. It leverages Lotka-Volterra dynamics to identify market regimes and applies rigorous risk management gates—including adaptive confidence thresholds, portfolio exposure limits, and VaR checks—to ensure high-probability trade execution and capital protection.

Use Cases

  • Regime-Adaptive Strategy Selection: Dynamically switch between mean-reversion (VWAP) and momentum strategies by analyzing normalized predator-prey dominance ratios rather than absolute thresholds.
  • Multi-Gate Risk Management: Protect capital by requiring all orders to pass through a series of validation gates, including GARCH-based adaptive confidence levels and portfolio risk limits.
  • Automated Trading Constraint Enforcement: Prevent unsupported trades, such as crypto short-selling on specific brokers, while enabling fractional share execution for precise position sizing.
  • Trading System Observability: Enhance debugging and monitoring with real-time dashboard indicators that explain exactly why specific signals were blocked, held, or approved by the gate system.
nametrading-gates-pattern-filter
description"Pattern filter normalization and multi-gate order entry system"
authorClaude Code
date2025-12-18

Trading Gates & Pattern Filter - Research Notes

Experiment Overview

Item Details
Date 2025-12-18
Goal Fix pattern filter blocking all trades; add dashboard gate visibility
Environment Python 3.10, Alpaca API, Live Trading
Status Success

Problem Statement

The live trader was showing "Pattern filter rejected - no high-win-rate pattern match" for all symbols, even when the dashboard showed valid BUY/SELL signals. Investigation revealed two issues:

  1. Missing predator/prey values: _get_predprey_validation() wasn't returning predator/prey values
  2. Misaligned thresholds: Pattern filter used absolute thresholds but Lotka-Volterra dynamics decay values to near-zero

Failed Attempts

Attempt Why It Failed
Lowering absolute thresholds (e.g., 0.01 instead of 0.4) Thresholds become meaningless when values vary by orders of magnitude
Using raw predator/prey values directly Values decay to 0.01-0.02, making both patterns fail
Bypassing pattern filter Defeats purpose of high-win-rate filtering

Solution: Normalized Dominance Ratios

The key insight is that while raw predator/prey values can be any magnitude, their ratio indicates regime dominance:

# In filter_by_patterns() - alpaca_trading/signals/pattern_filter.py

# Extract raw values
raw_prey = regime_context.get('prey_strength', 0.5)
raw_predator = regime_context.get('predator_strength', 0.5)

# Normalize to dominance ratios (0-1 range)
total_strength = raw_predator + raw_prey + 1e-9  # Avoid division by zero
prey_strength = raw_prey / total_strength  # Higher = more mean-reverting
predator_strength = raw_predator / total_strength  # Higher = more trending

Example

  • Raw values: predator=0.015, prey=0.17
  • Normalized: predator_dominance=8%, prey_dominance=92%
  • Interpretation: Strong mean-reverting regime (VWAP reversion pattern applies)

Trading Gates Architecture

Orders must pass ALL gates before execution:

Gate Threshold Implementation
Confidence Adaptive (0.50-0.65) get_adaptive_entry_threshold() based on GARCH regime
Pattern Filter 65% min win rate VWAP reversion (68%) OR momentum continuation (71%)
Crypto Short Block shorts detect_asset_type() == CRYPTO and signal < 0
Portfolio Limit <80% exposure portfolio_metrics.total_exposure
Capital Manager 30% safety buffer capital_mgr.check_trade_allowed()
Portfolio Risk VaR <2% portfolio_risk_mgr.check_risk()

Pattern Filter Requirements

VWAP Reversion (68% win rate):

  • prey_dominance >= 0.4 (mean-reverting regime)
  • Price 2+ standard deviations from VWAP
  • Signal direction matches expected reversion

Momentum Continuation (71% win rate):

  • predator_dominance >= 0.6 (trending regime)
  • trend_prob_up > 0.7 OR trend_prob_down > 0.7
  • Pullback in trend direction
  • Signal aligns with trend

Dashboard Gate Display

Added gate status indicators to plot_symbol_signals():

# In dashboard.py - alpaca_trading/visualization/dashboard.py

gate_statuses = symbol_signals.get('gate_statuses', [])
for i, sym in enumerate(symbols):
    gate = gate_statuses[i]
    status = gate.get('final_status', 'UNKNOWN')

    if status == 'READY':
        status_color = 'green'
        status_text = 'READY'
    elif status == 'BLOCKED':
        status_color = 'red'
        status_text = gate.get('block_reason', 'blocked')[:12]
    elif status == 'HOLD':
        status_color = 'gray'
        status_text = 'HOLD'

Key Files Modified

File Change
alpaca_trading/prediction/multi_tf_predictor.py Added predator/prey to _get_predprey_validation() return
alpaca_trading/signals/pattern_filter.py Normalized predator/prey to dominance ratios
alpaca_trading/executor.py Changed qty: int to qty: float for fractional shares
scripts/live_trader.py Added crypto short block, --dashboard flag
scripts/monitor_dashboard.py Added check_trading_gates() function
alpaca_trading/visualization/dashboard.py Added gate status display

Verification

After fix:

  • AAPL: PASS (vwap_reversion) - prey_dominance=92%
  • AMD: PASS (vwap_reversion) - prey_dominance=91.9%
  • AVGO: FAIL (no_match) - SELL signal in prey-dominant regime (expects BUY)
  • BTCUSD: FAIL (crypto_no_short) - Alpaca doesn't support crypto shorts

Key Learnings

  1. Lotka-Volterra dynamics decay: Both predator and prey values decay to near-zero over time; use ratios, not absolutes
  2. Crypto limitations: Alpaca doesn't support short selling for crypto; must block SELL signals for crypto entries
  3. Fractional shares: Executor must use float qty, not int, for proper fractional share support
  4. Dashboard visibility: Showing gate status helps debug why signals don't result in orders
  • position-reconciliation: Broker state sync
  • markov-regime-features: Debugging constant Markov features
  • drawdown-guardrails-pattern: Drawdown control across systems