# 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."

## 14. Flight9/BLUE Fill Learnings (Fable, 2026-07-16)

Real FLIGHT9/BLUE fill backfill — generalizable features, not hardcoded thresholds.

### Markout = Quality

Fill quality is measured by **post-fill markout** (price move N ticks after fill),
not just fill/no-fill. A maker fill at a good quoted price can still be a bad fill
if the market moves adversely after execution. The system scores fills by markout.

**Generalizable:** score fills by post-fill markout. Model fill-CONDITIONAL-on-adverse-flow.

### Fee Model (BingX, No Rebates)

BingX reports fees as NEGATIVE. MALKHUT convention: positive = cost.
- Maker fee: 2.0 bps (you pay)
- Taker fee: 5.0 bps (you pay)
- Fee savings: 3.0 bps (maker saves 3bp vs taker)
- There are NO rebates on BingX.

The system learns: prefer maker when fee savings > fill probability cost.

### Fill = Queue × Flow Intensity

Fill probability is driven by **trade-arrival intensity** (the tape), not the static book.
A resting maker fills only when trades print through its level for enough volume to clear
the queue ahead. The HftBacktestCWM queue model is the right substrate — it needs real
trade-flow intensity as input.

**On testnet:** maker fill-rate ~0% (no flow). This is an artifact, not a signal.

### Depth-for-Size, Not Spread

Spread alone LIES: an asset with 1.5bp spread behind ~$977 of depth is unfillable at any
size. Generalizable: key fill viability on **depth-within-K-bps** relative to order
notional, not spread.

### Measured Fees (Venue-Parameterized)

| Venue | Maker | Taker | Sign |
|-------|-------|-------|------|
| BingX | 2.00 bp | 5.016 bp | NEGATIVE = DEBIT |

Generalize: parameterize fee + sign per venue from measurement, never assume.

### Realized Friction (F9 VST Fills)

- Taker: ~20-27 bp adverse on thin/mid books
- Maker: saves ~3-4 bp WHEN it fills
- Maker fill-rate: ~0% on VST (no flow — testnet artifact)

### Counterfactual Maker Fill (Real Binance Book)

- ~50% fill on liquid books (at-touch upper bound)
- Queue + venue-thinness reduce it

### Validation Methodology

When validating against a crude fill sim, optimize on **RELATIVE lift** between two policies
through the SAME sim — fidelity bias cancels in the difference. Trust the ordering of
policies even when absolute fill rates are approximate.