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_velocity_calculation.py
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128
clp_auto_hedger/test_velocity_calculation.py
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#!/usr/bin/env python3
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"""
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Test script to demonstrate multi-timeframe velocity calculation (Option 3B)
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Shows how the new approach reduces false triggers while maintaining emergency response
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"""
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import time
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import random
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def simulate_velocity_calculation():
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"""Simulate the multi-timeframe velocity calculation"""
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print("=== Multi-Timeframe Velocity Calculation Demo ===\n")
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# Simulate price data with noise and occasional real moves
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base_price = 3000.0
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price_history = []
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velocity_history = []
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scenarios = [
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("Normal Trading", 10, 0.0002), # 0.02% noise
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("Noisy Market", 10, 0.0008), # 0.08% noise
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("Sharp Move", 5, 0.0025), # 0.25% move
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("Sustained Move", 10, 0.0010), # 0.1% sustained
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]
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for scenario_name, duration, max_change_pct in scenarios:
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print(f"Scenario: {scenario_name}")
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print(f"Duration: {duration}s, Max change per interval: {max_change_pct*100:.2f}%")
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print("-" * 50)
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current_price = base_price
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last_price = current_price
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price_history = [current_price]
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for i in range(duration):
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# Simulate price change
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change_pct = random.uniform(-max_change_pct, max_change_pct)
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current_price = current_price * (1 + change_pct)
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# Calculate velocities (same as implemented in clp_scalper_hedger.py)
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# 1-second velocity
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velocity_1s = (current_price - last_price) / last_price
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# 5-second average velocity
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velocity_5s = 0.0
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if len(price_history) >= 5:
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price_5s_ago = price_history[-5]
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velocity_5s = (current_price - price_5s_ago) / price_5s_ago / 5
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# Choose velocity (Option 3B logic)
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if abs(velocity_1s) > 0.002: # Extreme 1s move
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price_velocity = velocity_1s
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velocity_type = "1S_EXTREME"
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else: # Use smoothed 5s average
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price_velocity = velocity_5s
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velocity_type = "5S_SMOOTHED"
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# Current threshold (0.05% = 0.0005)
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VELOCITY_THRESHOLD_PCT = 0.0005
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trigger_emergency = abs(price_velocity) > VELOCITY_THRESHOLD_PCT
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print(f" Second {i+1:2d}: ${current_price:7.2f} | "
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f"Vel: {price_velocity*100:+6.3f}% ({velocity_type}) | "
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f"{'EMERGENCY' if trigger_emergency else 'Normal'}")
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# Update history
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price_history.append(current_price)
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last_price = current_price
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time.sleep(0.1) # Small delay for readability
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print(f"\nResults for {scenario_name}:")
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print(f" Emergency triggers: {sum(1 for i in range(len(price_history)) if abs(price_history[i]/price_history[max(0,i-1)] - 1) > 0.0005 and i > 0)}")
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print(f" Final price: ${current_price:.2f} ({((current_price/base_price)-1)*100:+.2f}%)")
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print("\n" + "="*60 + "\n")
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def compare_approaches():
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"""Compare old vs new velocity approach"""
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print("=== Approach Comparison ===\n")
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# Noisy price series that would trigger old approach falsely
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prices = [3000, 3001.5, 2998.5, 3002.0, 2999.0, 3003.0, 2997.0, 3001.0]
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print("Price series with 0.05% noise:", [f"${p:.2f}" for p in prices])
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print("\nOld Approach (1-second velocity only):")
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old_triggers = 0
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for i in range(1, len(prices)):
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old_velocity = (prices[i] - prices[i-1]) / prices[i-1]
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trigger = abs(old_velocity) > 0.0005
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if trigger:
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old_triggers += 1
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print(f" {i}: {old_velocity*100:+.3f}% {'EMERGENCY' if trigger else 'Normal'}")
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print(f"\nOld approach triggers: {old_triggers}")
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print("\nNew Approach (Multi-timeframe):")
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new_triggers = 0
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for i in range(1, len(prices)):
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if i >= 5:
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velocity_5s = (prices[i] - prices[i-5]) / prices[i-5] / 5
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final_velocity = velocity_5s
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velocity_type = "5S_SMOOTHED"
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else:
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final_velocity = (prices[i] - prices[i-1]) / prices[i-1]
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velocity_type = "1S_NORMAL"
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trigger = abs(final_velocity) > 0.0005
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if trigger:
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new_triggers += 1
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print(f" {i}: {final_velocity*100:+.3f}% ({velocity_type}) {'EMERGENCY' if trigger else 'Normal'}")
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print(f"\nNew approach triggers: {new_triggers}")
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print(f"\nReduction in false triggers: {old_triggers - new_triggers} ({((old_triggers-new_triggers)/old_triggers*100):.0f}%)")
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if __name__ == "__main__":
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print("Testing Multi-Timeframe Velocity Calculation for CLP Scalper Hedger\n")
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simulate_velocity_calculation()
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compare_approaches()
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print("\nKEY Benefits of Option 3B:")
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print(" • Reduces false triggers from normal 1-second noise")
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print(" • Maintains fast response to genuine sharp moves")
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print(" • Uses 5-second smoothing for sustained directional detection")
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print(" • Context-aware: distinguishes noise from real emergencies")
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print(" • Better suited for $8k position with lower risk appetite")
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