Deploy Hurst/VPIN directional strategy to live + paper

Live node:
  - Added Hurst VPIN to STRATEGIES (BTC, 0.00024 size, 00)
  - Feed BTC price into dollar-bar Hurst/VPIN every 5 ticks
  - Signal: BUY/SELL when H>0.55 + VPIN>0.25 + direction bias

Paper trader:
  - Added Kalman Pairs, Avellaneda-Stoikov, Hurst VPIN strategies
  - All 00 allocation, matching live node asset distribution
  - Hurst/VPIN signal from BTC mid-price dollar bars

Strategy file: hurst_vpin_live.py (lightweight price-tick mode)

strategies/hurst_vpin_live.py

@@ -0,0 +1,154 @@
+"""
+Hurst/VPIN integration module — provides compact signal generators
+for live trading, paper trading, and backtesting.
+
+Live: feeds price tick stream into Hurst dollar bars.
+Paper/Backtest: feeds real trade data.
+"""
+
+import math, time, numpy as np
+from collections import deque
+
+
+# ═══════════════════════════════════════════════════════════
+# 1. Hurst Exponent — R/S on log returns
+# ═══════════════════════════════════════════════════════════
+def _hurst_rs(returns: list) -> float:
+    """R/S estimate from log returns. Returns 0.20–0.80."""
+    n = len(returns)
+    if n < 32:
+        return 0.50
+    max_lag = min(n // 2, 64)
+    lags = []; rs = []
+    for lag in range(4, max_lag):
+        segs = n // lag
+        if segs < 2: continue
+        vals = []
+        for s in range(segs):
+            seg = returns[s*lag:(s+1)*lag]
+            mean = np.mean(seg)
+            dev = np.cumsum(seg - mean)
+            r = float(np.max(dev) - np.min(dev))
+            sd = float(np.std(seg, ddof=1))
+            if sd > 1e-12:
+                vals.append(r / sd)
+        if vals:
+            lags.append(np.log(lag))
+            rs.append(np.log(np.mean(vals)))
+    if len(lags) < 4:
+        return 0.50
+    slope = float(np.polyfit(lags, rs, 1)[0])
+    return max(0.20, min(0.80, slope))
+
+
+# ═══════════════════════════════════════════════════════════
+# 2. Dollar Bar Builder (notional-based)
+# ═══════════════════════════════════════════════════════════
+class DollarBar:
+    def __init__(self, threshold: float = 10000.0):
+        self.threshold = threshold
+        self.vol = 0.0
+        self.buy_vol = 0.0
+        self.sell_vol = 0.0
+        self.close = 0.0
+
+    def add(self, price: float, notional: float, is_buy: bool):
+        self.vol += notional
+        if is_buy:
+            self.buy_vol += notional
+        else:
+            self.sell_vol += notional
+        self.close = price
+
+    @property
+    def ready(self) -> bool:
+        return self.vol >= self.threshold
+
+    def emit(self) -> dict:
+        total = self.buy_vol + self.sell_vol
+        data = {
+            "close": self.close,
+            "vpin": abs(self.buy_vol - self.sell_vol) / total if total > 1 else 0.0,
+            "direction": (self.buy_vol - self.sell_vol) / total if total > 1 else 0.0,
+        }
+        self.vol = 0.0; self.buy_vol = 0.0; self.sell_vol = 0.0
+        return data
+
+
+# ═══════════════════════════════════════════════════════════
+# 3. Hurst/VPIN Signal (price-tick mode for live trading)
+# ═══════════════════════════════════════════════════════════
+class HurstVPINLive:
+    """Lightweight Hurst/VPIN for live price tick stream.
+    
+    Uses notional bars ($10K) from mid-price changes.
+    Each tick adds notional ≈ price * |Δprice| * 100 as volume proxy.
+    """
+    def __init__(self, threshold: float = 10000.0,
+                 hurst_window: int = 128,
+                 vpin_window: int = 50,
+                 hurst_entry: float = 0.55,
+                 vpin_threshold: float = 0.25):
+        self.threshold = threshold
+        self.vpin_window = vpin_window
+        self.hurst_entry = hurst_entry
+        self.vpin_threshold = vpin_threshold
+
+        self.bar = DollarBar(threshold)
+        self.vpin_buf = deque(maxlen=vpin_window)
+        self.vpin_dir_buf = deque(maxlen=vpin_window)
+        self.returns = deque(maxlen=hurst_window)
+        self.last_close = 0.0
+        self.last_price = 0.0
+
+    def feed_price(self, price: float):
+        """Feed a mid-price tick. Returns signal dict or None."""
+        if self.last_price <= 0:
+            self.last_price = price
+            return None
+
+        delta = price - self.last_price
+        is_buy = delta > 0
+        notional = price * abs(delta) * 100  # volume proxy
+        self.last_price = price
+
+        self.bar.add(price, notional, is_buy)
+        if not self.bar.ready:
+            return None
+
+        bar_data = self.bar.emit()
+
+        # VPIN
+        self.vpin_buf.append(bar_data["vpin"])
+        self.vpin_dir_buf.append(bar_data["direction"])
+        vpin = float(np.mean(self.vpin_buf)) if len(self.vpin_buf) >= self.vpin_window else 0.0
+        direction = float(np.mean(self.vpin_dir_buf)) if len(self.vpin_dir_buf) >= self.vpin_window else 0.0
+
+        # Hurst
+        if self.last_close > 0:
+            self.returns.append(math.log(bar_data["close"] / self.last_close))
+        self.last_close = bar_data["close"]
+
+        hurst = _hurst_rs(list(self.returns)) if len(self.returns) >= 64 else 0.50
+
+        # Signal
+        trending = hurst >= self.hurst_entry
+        high_vpin = vpin >= self.vpin_threshold
+
+        if trending and high_vpin:
+            if direction > 0.02:
+                return {"signal": "BUY", "hurst": round(hurst, 3), "vpin": round(vpin, 3), "direction": round(direction, 3)}
+            elif direction < -0.02:
+                return {"signal": "SELL", "hurst": round(hurst, 3), "vpin": round(vpin, 3), "direction": round(direction, 3)}
+
+        return None
+
+
+# ═══════════════════════════════════════════════════════════
+# 4. Hurst/VPIN for backtest (full trade data)
+# ═══════════════════════════════════════════════════════════
+from strategies.hurst_vpin import run_hurst_vpin, HurstVPINSignal
+
+# Expose for easy import
+def hurst_vpin_backtest(trades, capital=100.0, size=0.00024):
+    return run_hurst_vpin(trades, starting_capital=capital, size=size)