sentiment-engine/MALKHUT/docs/OB_MICROSTRUCTURE_STUDY.md

Order Book Microstructure Study — MALKHUT

Compiled from live Binance/BingX API data + academic literature

(Bouchaud, Cont/Stoikov, Cartea/Jaimungal)

Last updated: 2026-07-14

1. Order Book Depth — Power-Law Decay

The order book depth at distance d (in bps from mid) follows a power law:

D(d) = A * d^(1 - alpha)

where: A = amplitude (USD depth at 1 bps from mid) alpha = decay exponent (flatter = more depth at distance)

Per-Asset Parameters

Asset	A (amplitude USD)	alpha	Fragility	Depth@10bps USD	Depth@100bps USD	Template
BTC	750,000	0.70	0.10	5,983,000	20,000,000	institutional
ETH	600,000	0.75	0.12	2,095,000	7,200,000	institutional
SOL	400,000	0.85	0.18	954,000	4,000,000	mid_cap_l1
BNB	500,000	0.78	0.12	2,000,000	8,000,000	institutional
DOGE	22,000	1.00	0.30	432,000	2,772,000	retail_meme
ADA	60,000	0.90	0.20	110,000	1,500,000	mid_cap_l1
AVAX	55,000	0.88	0.18	107,000	1,200,000	mid_cap_l1
UNI	30,000	0.95	0.25	53,000	800,000	mid_cap_l1
LINK	80,000	0.87	0.17	129,000	1,800,000	mid_cap_l1
MATIC	35,000	0.92	0.22	55,000	900,000	mid_cap_l1
AAVE	20,000	0.95	0.25	35,000	600,000	mid_cap_l1
DOT	70,000	0.88	0.18	120,000	1,500,000	mid_cap_l1
ATOM	25,000	0.92	0.22	45,000	700,000	mid_cap_l1

Interpretation

• alpha < 0.80 = institutional blue-chip (BTC, ETH, BNB). Depth is spread relatively evenly. You can walk the book for $1M+ before seeing 10 bps slippage.

• alpha 0.85-0.92 = mid-cap L1 (SOL, ADA, AVAX, DOT, LINK). Decent depth at the touch, but thins rapidly. $100K order → 2-5 bps slippage.

• alpha >= 0.95 = retail/thin (UNI, DOGE, AAVE). Almost all depth at the top 1-2 levels. Any meaningful order walks the book significantly.

• Fragility factor = fraction of depth that vanishes during stress events. BTC loses 10% of depth; DOGE loses 30%. This is the "flash crash amplifier."

Depth During Stress

During market stress, depth collapses to fragility_factor * normal depth:

D_stress(d) = D_normal(d) * fragility_factor

For BTC: $750K * 0.10 = $75K at 1 bps during stress. For DOGE: $22K * 0.30 = $6.6K at 1 bps during stress.

Market makers pull within 1ms of flash crash onset. Recovery takes 30-300 seconds.

2. Spread Profiles

Asset	Normal (bps)	Stress Multiplier	Interpretation
BTC	0.01	50x	0.01 bps normal, 0.5 bps stress
ETH	0.02	50x	0.02 bps normal, 1.0 bps stress
BNB	0.50	15x	0.50 bps normal, 7.5 bps stress
SOL	1.26	8x	1.26 bps normal, 10.1 bps stress
DOGE	1.35	15x	1.35 bps normal, 20.3 bps stress
ADA	5.95	12x	5.95 bps normal, 71.4 bps stress
AVAX	1.48	10x	1.48 bps normal, 14.8 bps stress
UNI	2.76	12x	2.76 bps normal, 33.1 bps stress
LINK	1.25	8x	1.25 bps normal, 10.0 bps stress
MATIC	1.50	10x	1.50 bps normal, 15.0 bps stress
AAVE	2.50	12x	2.50 bps normal, 30.0 bps stress
DOT	1.00	8x	1.00 bps normal, 8.0 bps stress
ATOM	2.00	10x	2.00 bps normal, 20.0 bps stress

Key Insight for Strategy Design

BTC/ETH spreads are 10-500x tighter than alts. This means:

• BTC: spread cost is negligible; profitability depends on fill quality + adverse selection
• ADA/ATOM: spread cost is 10-60 bps round-trip; must capture >= spread to be profitable
• Stress spreads can be 50x normal for BTC — but that's still only 0.5 bps

3. Order Flow Characteristics

Asset	Orders/sec (normal)	Orders/sec (stress)	Cancel/Fill	Median $	P99 $	Avg Trade $
BTC	300	5,000	20.0	643	200,000	5,000
ETH	250	4,000	18.0	500	150,000	4,000
SOL	100	1,500	10.0	800	150,000	2,000
DOGE	80	800	8.0	96	52,000	200
ADA	60	600	7.0	200	40,000	500
AVAX	70	700	8.0	300	60,000	800
UNI	50	500	6.0	150	30,000	400
LINK	90	1,200	9.0	250	80,000	1,000
MATIC	55	550	7.0	180	35,000	400
AAVE	40	400	6.0	500	50,000	1,500
DOT	65	650	7.5	350	45,000	700
ATOM	45	450	6.5	200	35,000	500

Cancel/Fill Ratio Interpretation

• BTC 20x: For every fill, 20 orders are cancelled. This is pure HFT MM churn. The MM quotes aggressively, pulls when toxicity rises, re-quotes wider.
• DOGE 8x: Less MM activity, more genuine intent. Retail orders are more sticky.
• Alts 5-15x: Range between MM-dominated (higher) and retail-dominated (lower).

Order Size Distribution

All assets follow a power-law tail: log-normal body + Pareto tail.

• BTC P50 = $643 (median order), P99 = $200K. Tail exponent ~2.5.
• DOGE P50 = $96, P99 = $52K. Tail exponent ~2.0 (fatter tail = more whale orders).
• The P99 order is 300-600x the P50. These are institutional block trades.

Order Arrival Process

Orders arrive as a self-exciting Hawkes process (not Poisson):

• Clustering: a fill begets more fills within 10-100ms
• BTC: 300 orders/sec normal, 5000 during events (17x burst)
• Burst magnitude correlates with volatility regime

4. Market Maker Behavior

Asset	Max Inventory	Skew Tol.	Pull Speed	Margin
BTC	$10M	15 bps	3 ms	0.5 bps
ETH	$8M	15 bps	3 ms	0.5 bps
SOL	$5M	20 bps	10 ms	0.8 bps
BNB	$5M	20 bps	5 ms	0.6 bps
DOGE	$500K	40 bps	25 ms	2.0 bps
ADA	$1M	30 bps	20 ms	1.5 bps
AVAX	$1.5M	25 bps	15 ms	1.0 bps
UNI	$300K	50 bps	30 ms	2.5 bps
LINK	$2M	25 bps	12 ms	1.0 bps
MATIC	$400K	35 bps	22 ms	2.0 bps
AAVE	$200K	45 bps	28 ms	2.0 bps
DOT	$1.5M	25 bps	15 ms	1.0 bps
ATOM	$600K	35 bps	20 ms	1.5 bps

Key Dynamics

• Pull speed = how fast MM withdraws quotes after detecting toxicity. BTC MMs are 10x faster than DOGE MMs (3ms vs 25ms). This means:

  • BTC: you must be fast or you're picking off stale quotes
  • DOGE: stale quotes persist longer → latency arbitrage more viable

• Margin = minimum edge the MM requires to quote. BTC 0.5 bps = MM breaks even on a 0.5 bps spread after fees. DOGE 2.5 bps = MM needs 2.5 bps edge, because adverse selection is higher.

• Max inventory = position limit before MM widens quotes or stops quoting. BTC $10M vs DOGE $500K. Ratio is 20x, matching the depth ratio.

5. Volatility Regimes

Asset	Ann. Vol (normal)	Ann. Vol (crisis)	GARCH alpha	GARCH beta	Half-life
BTC	35%	100%	0.10	0.88	48 hrs
ETH	66.5%	130%	0.12	0.86	40 hrs
SOL	71.9%	150%	0.13	0.84	32 hrs
DOGE	77.9%	200%	0.15	0.82	24 hrs
BNB	50%	120%	0.11	0.87	42 hrs

GARCH Interpretation

• alpha + beta = persistence. BTC: 0.10 + 0.88 = 0.98. Very persistent. After a shock, volatility takes ~48 hours (half-life) to decay to 50%.
• Crisis vol is 2-3x normal vol. BTC goes from 35% → 100% annualized.
• Smaller caps have faster decay. DOGE half-life 24h vs BTC 48h. DOGE returns to calm faster but also spikes faster.

6. Intraday Patterns

Asset	Peak Hour (UTC)	Trough Hour (UTC)	Peak/Trough Ratio
BTC	15:00	19:00	7.4x
ETH	15:00	19:00	7.0x
DOGE	15:00	10:00	4.5x

Session Analysis

• US session (13:00-21:00 UTC): Highest volume, tightest spreads, deepest books. The 15:00 UTC peak = US market open overlap with EU close.
• Asia session (00:00-08:00 UTC): Lowest volume, widest spreads.
• Weekend: Volume drops 30-50%, vol drops to 0.65-0.70x, spreads widen 10-30%.

7. Cross-Asset Correlations

Pair	Normal	Crash	Interpretation
BTC-ETH	0.70-0.92	0.93-0.98	Near-perfect in crashes
BTC-SOL	0.60-0.80	0.85-0.95	High in crashes
BTC-DOGE	0.45-0.65	0.80-0.90	Moderate normal, high crash
BTC-LINK	0.55-0.75	0.85-0.92	Similar to SOL

"Correlations go to 1 in crashes"

This is the single most important portfolio-level fact. During normal times, diversification works. During crashes, EVERYTHING correlates with BTC. A "diversified" alt portfolio provides zero downside protection.

8. BingX-Specific Behavior vs Binance

Metric	BingX / Binance Ratio	Interpretation
Perp spread	1.7-12.6x wider	BingX has less MM competition
BTC depth	20x thinner	Much thinner books
Taker fee	1.25x higher	0.05% vs 0.04%
API latency	2-3x higher	100ms vs 40-50ms
Funding rate corr.	R² ~ 0.90, 0-8h lag	Use Binance as leading indicator
Spot spread	18-389x wider	NEVER use BingX spot

Practical Implications

• BingX is 10-20x harder to trade profitably than Binance for the same strategy. Thinner books + wider spreads + higher fees + higher latency.
• Cross-exchange arbitrage between BingX and Binance is real but latency-limited. The 0-8h funding rate lag creates opportunities.
• BingX perp is viable for market making (wider spread = more edge) but requires wider quotes and lower aggression.

9. Book Fragility & Cascade Dynamics

Flash Crash Anatomy

1. Trigger: Large market sell hits thin book (0.05x depth at that moment)
2. Cascade: Price drops through stop levels → forced liquidations → more selling
3. MM withdrawal: All MMs pull quotes within 1-5ms
4. Depth vacuum: Book goes from $750K to $75K (BTC) or $22K to $6.6K (DOGE)
5. Recovery: 30-300 seconds for MMs to re-quote, 10-60 minutes for depth to normalize

Per-Asset Cascade Characteristics

Asset	Trigger Price Drop	Liquidation Speed	Recovery
BTC	6.5%	slow	fast
ETH	4.0%	medium	medium
SOL	5.0%	medium	medium
DOGE	4.0%	fast	slow
UNI	3.5%	fast	slow
AAVE	4.0%	fast	slow

OI/MCap Ratio (Liquidation Pressure)

• BTC: 0.5% of market cap in open interest → low cascade risk
• DOGE: 1.4% → moderate cascade risk
• ETH: 1.9% → higher cascade risk
• The ratio directly predicts how much forced selling occurs per 1% price drop

Liquidation Trigger Threshold

The price drop needed to trigger cascade liquidations:

• BTC: 6.5% (hard to trigger → "too big to cascade")
• DOGE: 4.0% (easier to trigger → more volatile cascades)
• UNI: 3.5% (very easy to trigger → most fragile)

Recovery Asymmetry

Crashes are FAST (milliseconds for MM withdrawal, seconds for liquidations) but recovery is SLOW (minutes to hours for depth normalization). This asymmetry is exploitable: buy the dip 30-60 seconds after the crash, when depth is still thin but selling pressure is exhausted.

10. Retail vs Institutional Composition

Asset	Retail Ratio	Institutional Gap	Implication
BTC	0.35	0.04	Most institutional, best flows
ETH	0.40	0.06	Near-institutional
BNB	0.50	0.20	Balanced
SOL	0.72	0.75	Retail-dominated
DOGE	0.80	0.31	Heavily retail
UNI	0.60	0.25	Mixed
AAVE	0.55	0.20	Mixed

Trading Implications

• Institutional assets (BTC, ETH): Tighter spreads, deeper books, more efficient pricing. Edge comes from execution quality, not information.
• Retail assets (DOGE, SOL): Wider spreads, more predictable order flow, more stale-quote opportunities. Edge comes from toxicity detection + latency.
• The institutional gap = difference in quote persistence between institutional and retail orders. Higher gap = more predictable behavior = more exploitable.

11. Funding Rates (BingX Perps)

Asset	Mean (8h)	Std (8h)	Positive %	Basis Typical
BTC	0.59 bps	0.22 bps	100%	4.0 bps
ETH	0.50 bps	0.25 bps	95%	3.5 bps
SOL	0.30 bps	0.35 bps	65%	2.5 bps
DOGE	0.39 bps	0.34 bps	80%	2.0 bps
BNB	0.40 bps	0.25 bps	90%	3.0 bps
ADA	0.20 bps	0.40 bps	55%	1.5 bps
AVAX	0.15 bps	0.35 bps	50%	2.0 bps
UNI	0.10 bps	0.30 bps	45%	1.5 bps
LINK	0.25 bps	0.30 bps	60%	2.0 bps
MATIC	0.12 bps	0.32 bps	48%	1.8 bps
AAVE	0.08 bps	0.28 bps	40%	1.2 bps
DOT	0.18 bps	0.30 bps	55%	2.0 bps
ATOM	0.10 bps	0.28 bps	42%	1.5 bps

Key Insight

BTC funding is ALWAYS positive (100% of time) at ~0.59 bps/8h. This means:

• Longs ALWAYS pay shorts on BTC perps
• Being short BTC perp earns a steady 0.59 bps every 8 hours
• This is "free money" for short-biased strategies (which MALKHUT is)

The funding rate is the single most predictable return stream in crypto perps.

12. Expected Slippage Model

For a market order of size $X:

slippage_bps = sum_{d=1}^{D} (A * d^(-alpha)) for cumulative depth >= X

Example for BTC ($750K amplitude, alpha=0.70):

• $10K order: ~0.05 bps (negligible)
• $100K order: ~0.5 bps (one tick)
• $1M order: ~3.5 bps (walks the book meaningfully)
• $10M order: ~15 bps (aggressive, will move the market)

Example for DOGE ($22K amplitude, alpha=1.00):

• $1K order: ~0.5 bps
• $10K order: ~5 bps
• $100K order: ~50 bps (very aggressive, huge impact)

Implication

BTC allows $100K orders with <1 bps slippage. DOGE requires $1K orders for the same. Position sizing must account for the book's capacity, not just the strategy's signal.

13. Fill Quality Optimization (MALKHUT Core)

MALKHUT is an execution improvement engine. Fill quality IS the primary aim.

Fill Quality Metrics

For each CWM transition, MALKHUT computes:

• slippage_bps: aggressive fills — how far from mid?
• price_improvement_bps: passive fills — how much better than touch?
• levels_consumed: queue depth of fill
• post_fill_adverse_bps: price movement after fill (negative = adverse)
• fill_value_score: composite = quality - adverse * 0.5

How This Connects to the OB

The OB microstructure directly determines fill quality:

OB Characteristic	Impact on Fill Quality
Depth at touch	More depth = more fill opportunities for passive orders
Spread	Tighter spread = smaller price improvement possible
Depth decay (alpha)	Steeper decay = fills walk the book faster = higher slippage
Cancel/fill ratio	Higher ratio = more queue churn = harder to get fills
MM pull speed	Faster pull = stale quotes less likely = harder to snipe
Book fragility	During stress, depth drops 70-95% = fills at worse prices

Per-Asset Fill Quality Expectations

Asset	Expected Fill Quality	Why
BTC	Excellent	Deep book, tight spread, fast MM re-quote
ETH	Good	Similar to BTC, slightly thinner
SOL	Moderate	Mid-depth, moderate spread
DOGE	Poor	Thin book, wide spread, slow MM
ADA	Poor	Thin book, wide spread

Optimization Strategy

1. Venue selection: choose venues with better fill quality (Binance > BingX)
2. Order type selection: use POST_ONLY on tight-spread assets, MARKET on thin-spread
3. Offset optimization: CMA-ES learns optimal offset per (asset, regime, venue)
4. Size optimization: CMA-ES learns optimal size per (asset, regime, venue)
5. Timing optimization: CMA-ES learns when to quote vs when to wait

The PerformanceMatrix tracks avg_fill_value_score per (regime, strategy, venue), enabling the system to learn: "In this regime, on this venue, this strategy achieves the best fill quality."