Hawkes OFI + Deep LOB: two new strategies from advanced microstructure research
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ramseshk
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"""
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Deep Limit Order Book Strategy.
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Analyzes the full orderbook beyond top-of-book to detect:
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1. **Wall detection** — large resting orders that indicate support/resistance
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2. **Depth imbalance** — ratio of cumulative depth on bid vs ask side
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3. **Orderbook skew** — asymmetry in volume distribution across price levels
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4. **Thin-side prediction** — when one side of the book is thin, price likely moves that way
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When a large wall sits at a certain price level, the market is unlikely to
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break through it quickly. When the ask side is thin relative to the bid side,
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buying pressure is likely to push price up.
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Usage:
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from strategies.deep_lob import DeepLOB
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model = DeepLOB()
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signal = model.analyze(orderbook_bids, orderbook_asks, mark_price)
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"""
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import math
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from collections import deque
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class DeepLOB:
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"""Analyze full orderbook depth for trade signals."""
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def __init__(self, depth_levels: int = 10):
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self.depth_levels = depth_levels
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self.signal_history: deque = deque(maxlen=50)
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def analyze(self, bids: list, asks: list, mark_price: float) -> dict:
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"""Analyze orderbook and return a signal.
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Args:
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bids: list of [price, size] pairs sorted best→worst (descending)
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asks: list of [price, size] pairs sorted best→worst (ascending)
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mark_price: current mark price
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Returns:
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dict with signal, strength, and metrics
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"""
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if not bids or not asks or mark_price <= 0:
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return {"signal": None, "strength": 0.0}
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# 1. Cumulative depth on each side
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bid_depth = sum(sz for _, sz in bids[:self.depth_levels])
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ask_depth = sum(sz for _, sz in asks[:self.depth_levels])
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# 2. Wall detection — find largest single order on each side
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bid_walls = sorted([(px, sz) for px, sz in bids[:self.depth_levels]], key=lambda x: x[1], reverse=True)
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ask_walls = sorted([(px, sz) for px, sz in asks[:self.depth_levels]], key=lambda x: x[1], reverse=True)
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largest_bid_wall = bid_walls[0] if bid_walls else (0, 0)
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largest_ask_wall = ask_walls[0] if ask_walls else (0, 0)
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# 3. Wall-to-depth ratio — how concentrated is the orderbook?
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bid_concentration = largest_bid_wall[1] / bid_depth if bid_depth > 0 else 0
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ask_concentration = largest_ask_wall[1] / ask_depth if ask_depth > 0 else 0
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# 4. Depth-weighted mid price (more accurate than top-of-book mid)
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# Weight prices by their size to get "fair value"
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bid_weighted = sum(px * sz for px, sz in bids[:self.depth_levels]) / bid_depth if bid_depth > 0 else 0
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ask_weighted = sum(px * sz for px, sz in asks[:self.depth_levels]) / ask_depth if ask_depth > 0 else 0
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fair_price = (bid_weighted + ask_weighted) / 2 if bid_weighted > 0 and ask_weighted > 0 else mark_price
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# 5. Depth imbalance ratio
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total_depth = bid_depth + ask_depth
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depth_imbalance = (bid_depth - ask_depth) / total_depth if total_depth > 0 else 0
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# 6. Thin-side detection
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# If one side is much thinner, price likely moves that way
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depth_ratio = bid_depth / ask_depth if ask_depth > 0 else 999
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thin_side = None
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if depth_ratio > 3.0:
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thin_side = "ask" # Ask side thin → buyers will push through → bullish
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elif depth_ratio < 0.33:
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thin_side = "bid" # Bid side thin → sellers will push through → bearish
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# 7. Mark price vs fair value
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fv_deviation = (mark_price - fair_price) / fair_price if fair_price > 0 else 0
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# 8. Volume-weighted average spread
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vwap_spread = 0
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for i in range(min(len(bids), len(asks), 5)):
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spread_i = asks[i][0] - bids[i][0]
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vwap_spread += spread_i
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avg_spread = vwap_spread / min(len(bids), len(asks), 5) if bids and asks else 0
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# ═══════════════ Signal Generation ═══════════════
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signal = None
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strength = 0.0
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reasons = []
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# Wall imbalance signal
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if largest_bid_wall[1] > 2 * largest_ask_wall[1]:
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strength += 0.3
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reasons.append("large_bid_wall")
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elif largest_ask_wall[1] > 2 * largest_bid_wall[1]:
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strength -= 0.3
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reasons.append("large_ask_wall")
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# Depth imbalance signal
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if depth_imbalance > 0.4:
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strength += 0.25
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reasons.append("bid_depth_dominant")
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elif depth_imbalance < -0.4:
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strength -= 0.25
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reasons.append("ask_depth_dominant")
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# Thin-side prediction
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if thin_side == "ask":
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strength += 0.35
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reasons.append("thin_ask_pressure")
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elif thin_side == "bid":
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strength -= 0.35
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reasons.append("thin_bid_pressure")
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# Fair value deviation
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if fv_deviation > 0.002:
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strength -= 0.15 # Overpriced relative to weighted depth
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reasons.append("overvalued")
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elif fv_deviation < -0.002:
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strength += 0.15 # Underpriced
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reasons.append("undervalued")
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# Determine final signal
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threshold = 0.3
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if strength > threshold:
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signal = "BUY"
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elif strength < -threshold:
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signal = "SELL"
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self.signal_history.append({
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"signal": signal,
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"strength": strength,
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"depth_imbalance": round(depth_imbalance, 4),
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"thin_side": thin_side,
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})
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return {
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"signal": signal,
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"strength": round(abs(strength) if signal else strength, 4),
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"fair_price": round(fair_price, 1),
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"depth_imbalance": round(depth_imbalance, 4),
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"depth_ratio": round(depth_ratio, 2),
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"thin_side": thin_side,
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"bid_concentration": round(bid_concentration, 3),
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"ask_concentration": round(ask_concentration, 3),
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"avg_spread": round(avg_spread, 1),
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"largest_bid_wall": round(largest_bid_wall[1], 2),
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"largest_ask_wall": round(largest_ask_wall[1], 2),
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"reasons": reasons,
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}
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