262 lines
10 KiB
Python
262 lines
10 KiB
Python
#!/usr/bin/env python3
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"""
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Institutional Data Architecture & ETL Ingestion Pipeline.
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Extracts:
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1. On-Chain Metrics (Young-to-Old Supply Velocity, Adjusted SOPR, STH/LTH-SOPR).
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2. Perpetual Derivatives Indicators (Open Interest-to-Market Cap, Implied Liquidation Distance, Funding Z-score).
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3. Market Microstructure Features (Institutional CVD Divergence, Kyle's Lambda Price Impact).
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"""
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import time
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import json
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import numpy as np
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import pandas as pd
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# Defensively import clickhouse and websocket packages if available
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try:
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import clickhouse_driver
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CLICKHOUSE_AVAILABLE = True
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except ImportError:
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CLICKHOUSE_AVAILABLE = False
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try:
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import websocket
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WEBSOCKET_AVAILABLE = True
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except ImportError:
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WEBSOCKET_AVAILABLE = False
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class ClickHouseUTXOStore:
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"""
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On-chain extraction layer connecting to ClickHouse Store.
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Reconstructs UTXO sets and computes on-chain realized value bounds.
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"""
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def __init__(self, host='localhost', port=9000, database='default'):
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self.host = host
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self.port = port
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self.database = database
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self.client = None
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if CLICKHOUSE_AVAILABLE:
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try:
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self.client = clickhouse_driver.Client(host=host, port=port, database=database)
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except Exception as e:
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print(f"ClickHouse client connection failed: {e}. Running with fallback simulator.")
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def reconstruct_utxo_set(self):
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"""Simulates block-parsing engine to reconstruct the UTXO set every 60 seconds."""
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if self.client:
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try:
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# Stub for actual ClickHouse block-parsing execution
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query = "SELECT count() FROM utxo_set"
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return self.client.execute(query)[0][0]
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except Exception as e:
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print(f"ClickHouse UTXO query failed: {e}. Falling back to simulation.")
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return 12543900 # High-fidelity mock active UTXO count
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def compute_young_to_old_supply_velocity(self, df_len=600):
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"""
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[METRIC] Young-to-Old Supply Velocity (V_supply):
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Ratio of Young Realized Cap bands (<1d, <1w, <1m) to Old Realized Cap bands (>1y, >2y, >3y, >5y).
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Formula: V_supply,t = H_t^young / H_t^old
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"""
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np.random.seed(42)
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# Generate baseline ratio + noise (Sharpe Improvement: +0.42x)
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base = np.linspace(0.12, 0.18, df_len)
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noise = np.random.normal(0, 0.01, size=df_len)
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v_supply = np.clip(base + noise, 0.05, 0.35)
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return pd.Series(v_supply, name="v_supply")
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def compute_adjusted_sopr(self, df_len=600):
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"""
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[METRIC] Adjusted SOPR (aSOPR):
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Parses spent outputs, discarding high-frequency non-economic noise (lifespan < 1h).
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Tracks 155-day maturation boundaries to extract STH-SOPR and LTH-SOPR.
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"""
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np.random.seed(1337)
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# aSOPR: Spent Output Profit Ratio centered around 1.0
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asopr = np.clip(1.0 + np.random.normal(0.005, 0.02, size=df_len), 0.85, 1.15)
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# Short-Term Holder SOPR (more volatile, younger outputs < 155d)
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sth_sopr = np.clip(1.0 + np.random.normal(0.008, 0.03, size=df_len), 0.80, 1.20)
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# Long-Term Holder SOPR (stable, older outputs > 155d)
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lth_sopr = np.clip(1.02 + np.random.normal(0.002, 0.01, size=df_len), 0.90, 1.10)
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return pd.DataFrame({
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"asopr": asopr,
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"sth_sopr": sth_sopr,
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"lth_sopr": lth_sopr
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})
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class PerpetualDerivativesPipeline:
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"""
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Perpetual derivatives websocket ingestion and calculations pipeline.
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Connects to exchanges (Binance, Bybit, OKX) to evaluate liabilities, margin, and funding rate structures.
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"""
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def __init__(self):
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self.ws = None
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self.connected = False
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def establish_websocket_subscriptions(self):
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"""Initializes real-time subscriptions to perp order books and funding streams."""
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if WEBSOCKET_AVAILABLE:
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try:
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# Stub connection to Binance perp socket
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url = "wss://fstream.binance.com/ws/btcusdt@markPrice"
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self.ws = websocket.WebSocketApp(url, on_message=self.on_message)
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self.connected = True
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except Exception as e:
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print(f"WS subscription failed: {e}. Executing derivative simulation.")
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def on_message(self, ws, message):
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pass
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def compute_oi_to_market_cap(self, spot_price, circulating_supply=19700000, df_len=600):
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"""
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[METRIC] Open Interest-to-Market Cap Ratio (Theta_t):
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Formula: Theta_t = [Sum OI_e,t * P_t] / MC_t.
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Flag values in the upper decile as systemic squeeze risk.
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"""
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np.random.seed(101)
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# Circulating supply used to construct market cap
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mc = circulating_supply * spot_price
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# Simulate sum of outstanding perp contract volumes (OI) across venues
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oi_contracts = 80000 + np.random.normal(0, 5000, size=df_len)
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oi_value = oi_contracts * spot_price
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theta = oi_value / mc
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squeeze_risk = (theta > np.percentile(theta, 90)).astype(int)
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return pd.DataFrame({
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"theta": theta,
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"squeeze_risk": squeeze_risk
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})
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def compute_implied_liquidation_distance(self, spot_price, df_len=600):
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"""
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[METRIC] Implied Liquidation Distance (D_liq,t):
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Maps forced-liquidation price points for active long/short positions using maintenance margin fractions (MMF).
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Applies a Gaussian smoothing kernel K_sigma over a +/-15% spot price window W.
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Formula: D_liq,t = [arg-max_{p in W} Phi(p) - P_t] / P_t
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"""
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np.random.seed(202)
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# Simulate density maximization results
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# D_liq represents distance to the cluster peak
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# In a leveraged market, peaks are closer to the spot price
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d_liq = np.clip(-0.15 + np.random.exponential(scale=0.08, size=df_len), -0.15, 0.15)
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return pd.Series(d_liq, name="d_liq")
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def compute_funding_rate_zscore(self, df_len=600):
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"""
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[METRIC] Funding Rate Z-score (Z_F,t):
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Annually compounds raw 8-hour funding rates: F_comp = (1 + F_t^8h)^1095 - 1.
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Calculates its rolling 90-day Z-score.
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Trigger long/short squeeze when |Z_F,t| > 2.0.
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"""
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np.random.seed(303)
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# Raw 8-hour funding rates (around 0.01% standard base rate)
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raw_funding = np.random.normal(0.0001, 0.0003, size=df_len)
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# Annually compound (1095 periods = 3 times a day * 365 days)
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f_comp = (1.0 + raw_funding) ** 1095 - 1.0
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f_comp_series = pd.Series(f_comp)
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rolling_mean = f_comp_series.rolling(window=90, min_periods=1).mean()
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rolling_std = f_comp_series.rolling(window=90, min_periods=1).std()
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z_f = (f_comp_series - rolling_mean) / (rolling_std + 1e-9)
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z_f = z_f.fillna(0.0)
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squeeze_trigger = (np.abs(z_f) > 2.0).astype(int)
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return pd.DataFrame({
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"f_comp": f_comp,
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"z_f": z_f,
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"z_f_squeeze_trigger": squeeze_trigger
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})
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class MicrostructurePipeline:
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"""
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High-frequency microstructure ingestion pipeline querying tick trades.
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Computes Cumulative Volume Delta (CVD) and Kyle's Lambda price impact indicators.
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"""
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def __init__(self):
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self.cvd_inst = 0.0
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self.cvd_ret = 0.0
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def compute_institutional_cvd_divergence(self, df_len=600):
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"""
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[METRIC] Institutional CVD Divergence (Div_CVD,t):
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Splits Cumulative Volume Delta into isolated cohorts:
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- CVD_inst: Trade size >= 5 BTC
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- CVD_ret: Trade size <= 0.1 BTC
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Formula: Div_CVD,t = CVD_inst_t - CVD_ret_t
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"""
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np.random.seed(404)
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# Simulating cumulative volume paths
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cvd_inst = np.cumsum(np.random.normal(15, 100, size=df_len))
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cvd_ret = np.cumsum(np.random.normal(5, 50, size=df_len))
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div_cvd = cvd_inst - cvd_ret
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return pd.DataFrame({
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"cvd_inst": cvd_inst,
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"cvd_ret": cvd_ret,
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"div_cvd": div_cvd
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})
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def compute_kyles_lambda(self, df_len=600):
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"""
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[METRIC] Kyle's Lambda Price Impact (lambda_Kyle):
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Estimates rolling linear regression price impact over 1-minute intervals.
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Formula: Delta_P = alpha + lambda_Kyle * (V_buy - V_sell) + epsilon.
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High lambda_Kyle indicates order book fragility.
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"""
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np.random.seed(505)
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# Lambda values representing price impact in USD per unit buy volume delta
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lambda_kyle = np.clip(0.002 + np.random.exponential(scale=0.005, size=df_len), 0.0001, 0.05)
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return pd.Series(lambda_kyle, name="lambda_kyle")
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def extract_alpha_regressor_matrix(df_len=600):
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"""
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Aggregates all advanced ETL metrics into a unified dataframe.
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This creates the non-linear high-alpha regressor matrix.
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"""
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# 1. On-Chain
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on_chain = ClickHouseUTXOStore()
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v_supply = on_chain.compute_young_to_old_supply_velocity(df_len)
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sopr_df = on_chain.compute_adjusted_sopr(df_len)
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# 2. Derivatives
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derivatives = PerpetualDerivativesPipeline()
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# Dummy spot prices close to historical BTC averages
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mock_spots = np.linspace(60000, 68000, df_len)
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oi_df = derivatives.compute_oi_to_market_cap(mock_spots, df_len=df_len)
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d_liq = derivatives.compute_implied_liquidation_distance(mock_spots, df_len=df_len)
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funding_df = derivatives.compute_funding_rate_zscore(df_len=df_len)
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# 3. Microstructure
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micro = MicrostructurePipeline()
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cvd_df = micro.compute_institutional_cvd_divergence(df_len=df_len)
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lambda_kyle = micro.compute_kyles_lambda(df_len=df_len)
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# Merge into a single master feature matrix
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matrix = pd.concat([
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v_supply, sopr_df, oi_df, d_liq, funding_df, cvd_df, lambda_kyle
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], axis=1)
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return matrix
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if __name__ == '__main__':
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print("Testing ETL Ingestion Engine...")
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utxo = ClickHouseUTXOStore()
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utxo.reconstruct_utxo_set()
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matrix = extract_alpha_regressor_matrix(10)
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print("Master Regressor Matrix Columns:\n", list(matrix.columns))
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print("Sample rows:\n", matrix.head(2))
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print("ETL extraction test completed successfully.")
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