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/velocity_config.py
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clp_auto_hedger/velocity_config.py
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#!/usr/bin/env python3
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"""
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Configuration module for enhanced velocity calculations in CLP Scalper Hedger
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Provides configurable parameters for multi-timeframe velocity detection
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"""
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from dataclasses import dataclass, field
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from typing import Dict, List, Optional
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import json
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import os
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@dataclass
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class VelocityTimeframe:
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"""Configuration for a single velocity timeframe"""
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name: str
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periods: int # Number of periods to average over
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weight: float # Weight in decision making (0.0 to 1.0)
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threshold: float # Velocity threshold for this timeframe
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description: str
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@dataclass
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class VelocityConfig:
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"""Enhanced velocity configuration with multiple timeframes and market conditions"""
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# Basic settings
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max_velocity_cap: float = 0.5 # Cap at 50% change per interval
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history_length: int = 60 # Keep last 60 price points for calculations
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# Timeframe configurations
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timeframes: Optional[List[VelocityTimeframe]] = None
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# Market condition thresholds
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normal_threshold: float = 0.0005 # 0.05% for normal markets
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volatile_threshold: float = 0.001 # 0.1% for volatile markets
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extreme_threshold: float = 0.002 # 0.2% for extreme markets
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# Emergency detection settings
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extreme_move_threshold: float = 0.002 # 0.2% for immediate response
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sustained_move_periods: int = 5 # Periods for sustained move detection
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# Smoothing settings
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use_ema_smoothing: bool = True
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ema_alpha: float = 0.2 # EMA smoothing factor
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# Edge proximity for velocity triggers
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edge_proximity_factor: float = 0.05 # 5% from range edge
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def __post_init__(self):
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"""Initialize default timeframes if not provided"""
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if self.timeframes is None:
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self.timeframes = [
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VelocityTimeframe(
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name="1s",
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periods=1,
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weight=0.4,
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threshold=self.extreme_threshold,
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description="Instantaneous velocity for emergency detection"
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),
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VelocityTimeframe(
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name="5s",
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periods=5,
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weight=0.3,
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threshold=self.normal_threshold,
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description="Short-term smoothed velocity"
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),
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VelocityTimeframe(
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name="10s",
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periods=10,
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weight=0.2,
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threshold=self.normal_threshold * 0.8,
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description="Medium-term trend detection"
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),
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VelocityTimeframe(
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name="30s",
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periods=30,
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weight=0.1,
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threshold=self.normal_threshold * 0.6,
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description="Long-term sustained moves"
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)
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]
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@classmethod
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def conservative(cls) -> 'VelocityConfig':
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"""Conservative configuration for low-risk trading"""
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config = cls()
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config.normal_threshold = 0.0003 # 0.03%
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config.volatile_threshold = 0.0006 # 0.06%
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config.extreme_threshold = 0.001 # 0.1%
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config.extreme_move_threshold = 0.001 # 0.1%
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return config
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@classmethod
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def aggressive(cls) -> 'VelocityConfig':
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"""Aggressive configuration for high-frequency trading"""
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config = cls()
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config.normal_threshold = 0.001 # 0.1%
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config.volatile_threshold = 0.002 # 0.2%
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config.extreme_threshold = 0.003 # 0.3%
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config.extreme_move_threshold = 0.003 # 0.3%
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return config
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@classmethod
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def from_file(cls, config_path: str) -> 'VelocityConfig':
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"""Load configuration from JSON file"""
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if not os.path.exists(config_path):
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raise FileNotFoundError(f"Configuration file not found: {config_path}")
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with open(config_path, 'r') as f:
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data = json.load(f)
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# Reconstruct VelocityTimeframe objects
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if 'timeframes' in data and data['timeframes'] is not None:
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data['timeframes'] = [VelocityTimeframe(**tf) for tf in data['timeframes']]
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return cls(**data)
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def to_file(self, config_path: str) -> None:
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"""Save configuration to JSON file"""
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data = {
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'max_velocity_cap': self.max_velocity_cap,
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'history_length': self.history_length,
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'timeframes': [
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{
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'name': tf.name,
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'periods': tf.periods,
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'weight': tf.weight,
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'threshold': tf.threshold,
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'description': tf.description
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} for tf in self.timeframes or []
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],
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'normal_threshold': self.normal_threshold,
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'volatile_threshold': self.volatile_threshold,
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'extreme_threshold': self.extreme_threshold,
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'extreme_move_threshold': self.extreme_move_threshold,
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'sustained_move_periods': self.sustained_move_periods,
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'use_ema_smoothing': self.use_ema_smoothing,
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'ema_alpha': self.ema_alpha,
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'edge_proximity_factor': self.edge_proximity_factor
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}
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# Only create directory if path contains directory
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config_dir = os.path.dirname(config_path)
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if config_dir:
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os.makedirs(config_dir, exist_ok=True)
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with open(config_path, 'w') as f:
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json.dump(data, f, indent=2)
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def get_active_threshold(self, market_volatility: float) -> float:
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"""Get appropriate threshold based on market volatility"""
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if market_volatility < 0.001: # Very low volatility
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return self.normal_threshold
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elif market_volatility < 0.003: # Normal volatility
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return self.volatile_threshold
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else: # High volatility
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return self.extreme_threshold
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def create_default_config() -> VelocityConfig:
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"""Create default velocity configuration"""
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return VelocityConfig()
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def create_config_files() -> None:
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"""Create example configuration files"""
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configs = {
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'velocity_config_conservative.json': create_default_config().conservative(),
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'velocity_config_normal.json': create_default_config(),
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'velocity_config_aggressive.json': create_default_config().aggressive()
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}
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for filename, config in configs.items():
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config.to_file(filename)
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if __name__ == "__main__":
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# Example usage and config file creation
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print("Creating velocity configuration files...")
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create_config_files()
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print("Configuration files created successfully!")
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# Display default configuration
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default_config = create_default_config()
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print(f"\nDefault configuration:")
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print(f"Normal threshold: {default_config.normal_threshold*100:.3f}%")
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if default_config.timeframes:
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print(f"Timeframes: {len(default_config.timeframes)}")
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for tf in default_config.timeframes:
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print(f" - {tf.name}: {tf.periods} periods, {tf.threshold*100:.3f}% threshold, {tf.weight:.1f} weight")
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