119 lines
4.4 KiB
Python
119 lines
4.4 KiB
Python
"""
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Flow Decay & Reversal Validation Service
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Validates flow decay/reversal signals with anchors (premium, dealer pressure, price levels)
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"""
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import pandas as pd
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import numpy as np
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from typing import Optional
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from enum import Enum
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from utils.logger import logger
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class FlowState(Enum):
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"""Flow state classification"""
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ACTIONABLE = "ACTIONABLE" # Flow decay/reversal is actionable (trade signal)
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INFORMATIONAL = "INFORMATIONAL" # Flow decay/reversal is informational only (no trade signal)
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class FlowDecayValidator:
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"""
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Validates flow decay and reversal signals.
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Flow decay/reversal is actionable ONLY IF:
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- Premium contracts (high relative premium)
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- Dealer hedge pressure decreases
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- Price fails near VWAP / opening range / key level
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Otherwise mark as INFORMATIONAL.
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"""
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def __init__(self):
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# Configuration
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self.min_relative_premium_for_actionable = 60.0 # Minimum relative premium score
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self.vwap_failure_threshold_pct = 0.5 # Price within 0.5% of VWAP = failure
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self.dealer_pressure_decrease_threshold = 20.0 # Dealer pressure decrease threshold
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def validate_flow_decay_reversal(
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self,
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df: pd.DataFrame,
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row_idx: int
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) -> str:
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"""
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Validate if flow decay/reversal is actionable.
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Returns FlowState enum value as string.
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"""
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if row_idx >= len(df):
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return FlowState.INFORMATIONAL.value
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current_row = df.iloc[row_idx]
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# Check 1: Premium contracts (relative premium score)
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relative_premium_score = current_row.get('relative_premium_score', 0) or 0
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if relative_premium_score < self.min_relative_premium_for_actionable:
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return FlowState.INFORMATIONAL.value
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# Check 2: Dealer hedge pressure decreases
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# Look at recent dealer pressure trend
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symbol = current_row.get('symbol_norm')
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flow_ts_utc = current_row.get('flow_ts_utc')
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if pd.notna(symbol) and pd.notna(flow_ts_utc):
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from datetime import timedelta
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window_start = flow_ts_utc - timedelta(minutes=30)
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mask = (
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(df['symbol_norm'] == symbol.upper()) &
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(df['flow_ts_utc'] >= window_start) &
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(df['flow_ts_utc'] <= flow_ts_utc) &
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(df['dealer_hedge_pressure_score'].notna())
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)
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recent_pressure = df[mask]['dealer_hedge_pressure_score']
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if len(recent_pressure) >= 2:
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current_pressure = current_row.get('dealer_hedge_pressure_score', 0) or 0
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recent_avg = recent_pressure.iloc[:-1].mean()
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pressure_decrease = recent_avg - current_pressure
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if pressure_decrease < self.dealer_pressure_decrease_threshold:
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return FlowState.INFORMATIONAL.value
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# Check 3: Price fails near VWAP / opening range / key level
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price_vs_vwap_pct = current_row.get('price_vs_vwap_pct')
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pct_vs_rth_open = current_row.get('pct_vs_rth_open')
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price_failure = False
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# VWAP failure
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if price_vs_vwap_pct is not None and not pd.isna(price_vs_vwap_pct):
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if abs(price_vs_vwap_pct) <= self.vwap_failure_threshold_pct:
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price_failure = True
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# Opening range failure
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if pct_vs_rth_open is not None and not pd.isna(pct_vs_rth_open):
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if abs(pct_vs_rth_open) <= 0.3: # Within 0.3% of open
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price_failure = True
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if not price_failure:
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return FlowState.INFORMATIONAL.value
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# All checks passed: actionable
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return FlowState.ACTIONABLE.value
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def enrich_with_flow_state(self, df: pd.DataFrame) -> pd.DataFrame:
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"""
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Add flow state validation to DataFrame.
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Adds: flow_state
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"""
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if df.empty:
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return df
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df = df.copy()
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df['flow_state'] = FlowState.ACTIONABLE.value # Default to actionable
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# Only validate flow decay/reversal cases
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# For now, mark all as actionable (can be enhanced based on flow_acceleration, follow_on_ratio)
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# This is a placeholder - in production, you'd identify decay/reversal patterns first
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logger.info(f"Flow state validation complete. Actionable: {(df['flow_state'] == FlowState.ACTIONABLE.value).sum()}")
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return df
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