Restructured hedger modules: moved CLP hedger and auto hedger into separate folders, updated data fetchers and main app, removed deprecated files
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DiTus
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clp_auto_hedger/test_enhanced_velocity.py
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clp_auto_hedger/test_enhanced_velocity.py
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#!/usr/bin/env python3
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"""
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Enhanced test script for multi-timeframe velocity calculation with configurable thresholds
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Demonstrates the new EnhancedVelocityCalculator capabilities
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"""
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import time
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import random
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import logging
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from enhanced_velocity_calculator import EnhancedVelocityCalculator, VelocityThresholdAnalyzer
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from velocity_config import VelocityConfig, create_default_config, VelocityTimeframe
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# Set up logging
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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def create_test_scenarios():
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"""Create different market scenarios for testing"""
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base_price = 3000.0
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scenarios = {
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"Normal Trading": {
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"duration": 20,
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"noise_level": 0.0002, # 0.02% noise
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"trend": 0.0,
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"description": "Normal market conditions with small random fluctuations"
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},
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"Noisy Market": {
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"duration": 20,
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"noise_level": 0.0008, # 0.08% noise
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"trend": 0.0,
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"description": "High volatility with large random movements"
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},
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"Sharp Flash Crash": {
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"duration": 10,
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"noise_level": 0.0001,
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"trend": -0.015, # 1.5% downward over duration
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"description": "Sudden sharp price drop (emergency scenario)"
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},
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"Sustained Uptrend": {
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"duration": 30,
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"noise_level": 0.0003,
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"trend": 0.002, # 0.2% upward per interval
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"description": "Gradual sustained upward movement"
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},
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"Whale Manipulation": {
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"duration": 15,
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"noise_level": 0.0005,
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"spike_magnitude": 0.008, # 0.8% sudden spike
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"spike_timing": 8,
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"description": "Large player creates artificial spike"
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}
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}
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return base_price, scenarios
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def test_enhanced_velocity_calculation():
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"""Test the enhanced velocity calculator with different scenarios"""
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print("=== Enhanced Multi-Timeframe Velocity Calculator Demo ===\n")
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# Create enhanced configuration
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config = create_default_config()
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calculator = EnhancedVelocityCalculator(config)
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base_price, scenarios = create_test_scenarios()
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for scenario_name, params in scenarios.items():
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print(f"Scenario: {scenario_name}")
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print(f"Description: {params['description']}")
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print("-" * 60)
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current_price = base_price
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total_triggers = 0
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emergency_overrides = 0
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for i in range(params["duration"]):
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# Generate price movement
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noise = random.uniform(-params["noise_level"], params["noise_level"])
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trend_component = params.get("trend", 0)
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# Handle special spike scenario
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if "spike_magnitude" in params and i == params["spike_timing"]:
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price_change = params["spike_magnitude"]
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print(f" *** SPIKE at second {i+1}!")
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else:
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price_change = noise + trend_component
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# Apply price change
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current_price = current_price * (1 + price_change)
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# Calculate enhanced velocity signal
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signal = calculator.update_price(current_price)
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# Check for triggers
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if signal.recommendation in ["trigger_protection", "emergency_override"]:
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total_triggers += 1
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if signal.recommendation == "emergency_override":
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emergency_overrides += 1
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trigger_type = "EMERGENCY" if signal.recommendation == "emergency_override" else "PROTECTION"
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print(f" Second {i+1:2d}: ${current_price:7.2f} | "
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f"Vel: {signal.final_velocity*100:+6.3f}% ({signal.dominant_timeframe}) | "
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f"{trigger_type}")
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elif abs(signal.final_velocity) > 0.0001: # Show interesting movements
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print(f" Second {i+1:2d}: ${current_price:7.2f} | "
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f"Vel: {signal.final_velocity*100:+6.3f}% ({signal.dominant_timeframe}) | "
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f"Conf: {signal.confidence:.2f} | {signal.market_condition}")
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time.sleep(0.05) # Small delay for readability
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print(f"\nResults for {scenario_name}:")
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print(f" Total velocity triggers: {total_triggers}")
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print(f" Emergency overrides: {emergency_overrides}")
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print(f" Final price: ${current_price:.2f} ({((current_price/base_price)-1)*100:+.2f}%)")
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# Get velocity summary
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summary = calculator.get_velocity_summary()
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print(f" Market volatility: {summary['market_volatility']*100:.3f}%")
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print("\n" + "="*70 + "\n")
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def test_threshold_optimization():
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"""Test threshold optimization with historical data"""
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print("=== Threshold Optimization Analysis ===\n")
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# Generate synthetic historical data
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base_price = 3000.0
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historical_data = []
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current_price = base_price
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# Mix of different market conditions
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for _ in range(100):
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# Randomly choose market condition
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condition = random.choice(["normal", "volatile", "flash_crash", "trend"])
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if condition == "normal":
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change = random.uniform(-0.0002, 0.0002)
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elif condition == "volatile":
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change = random.uniform(-0.0008, 0.0008)
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elif condition == "flash_crash":
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change = random.uniform(-0.01, -0.001)
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else: # trend
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change = random.uniform(0.0001, 0.0005)
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current_price = current_price * (1 + change)
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historical_data.append(current_price)
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# Test different threshold configurations
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configs = {
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"Conservative": create_default_config().conservative(),
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"Normal": create_default_config(),
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"Aggressive": create_default_config().aggressive()
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}
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thresholds_to_test = [0.0003, 0.0005, 0.0008, 0.001, 0.0015, 0.002]
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for config_name, config in configs.items():
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print(f"Testing {config_name} Configuration:")
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print(f"Normal threshold: {config.normal_threshold*100:.3f}%")
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calculator = EnhancedVelocityCalculator(config)
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analyzer = VelocityThresholdAnalyzer(calculator)
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# Reset calculator for clean test
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calculator.price_history = []
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for tf_name in calculator.velocity_history:
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calculator.velocity_history[tf_name] = []
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results = analyzer.analyze_threshold_performance(historical_data, thresholds_to_test)
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print(f"Optimal threshold: {results['optimal_threshold']*100:.3f}%")
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print(f"Performance: {results['optimal_performance']}")
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print(f"Recommendation: {results['recommendation']}\n")
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def test_different_timeframe_configs():
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"""Test different timeframe configurations"""
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print("=== Timeframe Configuration Comparison ===\n")
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# Custom timeframe configurations
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quick_response_config = create_default_config()
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quick_response_config.timeframes = [
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VelocityTimeframe("1s", 1, 0.6, 0.002, "Emergency detection"),
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VelocityTimeframe("3s", 3, 0.3, 0.001, "Quick response"),
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VelocityTimeframe("10s", 10, 0.1, 0.0005, "Trend confirmation")
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]
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smooth_averaging_config = create_default_config()
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smooth_averaging_config.timeframes = [
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VelocityTimeframe("5s", 5, 0.3, 0.0008, "Short-term smoothing"),
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VelocityTimeframe("15s", 15, 0.4, 0.0005, "Medium-term smoothing"),
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VelocityTimeframe("30s", 30, 0.3, 0.0003, "Long-term smoothing")
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]
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configs = {
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"Quick Response": quick_response_config,
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"Smooth Averaging": smooth_averaging_config,
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"Default Balanced": create_default_config()
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}
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# Test with flash crash scenario
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base_price = 3000.0
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current_price = base_price
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for config_name, config in configs.items():
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calculator = EnhancedVelocityCalculator(config)
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print(f"Testing {config_name} Configuration:")
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# Simulate flash crash
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for i in range(10):
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if i == 3: # Flash crash at second 4
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price_change = -0.01 # 1% drop
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elif i >= 4 and i <= 6: # Continued drop
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price_change = -0.003
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else:
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price_change = random.uniform(-0.0002, 0.0002)
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current_price = current_price * (1 + price_change)
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signal = calculator.update_price(current_price)
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if signal.recommendation in ["trigger_protection", "emergency_override"]:
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trigger_time = i + 1
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trigger_velocity = signal.final_velocity * 100
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trigger_timeframe = signal.dominant_timeframe
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print(f" *** Trigger at second {trigger_time}: {trigger_velocity:+.3f}% ({trigger_timeframe})")
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break
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else:
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print(" No trigger detected")
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print()
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def main():
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"""Run all enhanced velocity calculation tests"""
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print("Enhanced Multi-Timeframe Velocity Calculator Testing\n")
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print("="*70)
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test_enhanced_velocity_calculation()
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test_threshold_optimization()
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test_different_timeframe_configs()
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print("KEY Benefits of Enhanced Velocity Calculator:")
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print(" • Configurable multi-timeframe analysis")
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print(" • Market-adaptive thresholds")
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print(" • EMA smoothing for noise reduction")
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print(" • Confidence-based decision making")
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print(" • Comprehensive performance analysis")
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print(" • Flexible configuration for different risk profiles")
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print("\nThe enhanced system is ready for production deployment!")
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if __name__ == "__main__":
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main()
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