market-microstructure

# Market Microstructure ## Overview Study the micro-level mechanisms of price formation: who is trading, how they are trading, and how trades affect prices. For quantitative strategies, this matters because it improves transaction-cost estimation, identifies informed trading, and optimizes execution. Applicable scenarios: - Precise estimation of strategy trading costs (instead of simply assuming a flat 0.1% fee) - Designing large-order execution strategies (`TWAP / VWAP / IS`) - Detecting order-flow toxicity (avoid time windows dominated by informed traders) - Quantifying liquidity risk (flash-crash warning) - Capturing China A-share-specific microstructure features (call auction / closing auction / block trades) ## Core Concepts ### Bid-Ask Spread **Three measurements:** | Metric | Formula | Meaning | |------|------|------| | Quoted spread | `Ask - Bid` | Best spread shown in the limit order book | | Effective spread | `2 × |trade price - mid price|` | Actual spread paid by the trader | | Realized spread | `2 × direction × (trade price - mid price 5min later)` | True market-maker profit | ``` China A-share example: Instrument: 600519.SH Kweichow Moutai Best bid: 1680.00 Best ask: 1680.50 Quoted spread: 0.50 RMB = 0.03% Instrument: 000001.SZ Ping An Bank Best bid: 11.05 Best ask: 11.06 Quoted spread: 0.01 RMB = 0.09% Spread decomposition (Roll): Spread = adverse-selection cost + inventory cost + order-processing cost In China A-shares: adverse selection accounts for 60-70% (mixture of retail and informed traders) Spread drivers: - Larger market cap -> smaller spread (Moutai 0.03% vs small-cap 0.5%) - Higher volatility -> wider spread (market-maker risk premium) - Higher volume -> narrower spread (greater competition) - Higher information asymmetry -> wider spread (adverse selection) ``` ### Order-Flow Toxicity Metrics **VPIN (Volume-Synchronized Probability of Informed Trading):** ``` Principle: replace clock time with volume time to measure the probability of informed trading Calculation steps: 1. Bucket trades by fixed volume (Volume Bucket) Bucket size V = average daily volume / 50 (about 5-10 minutes per bucket) 2. Classify buy and sell volume in each bucket (Bulk Volume Classification): buy_volume = V × Φ(ΔP / σ) (standard normal CDF) sell_volume = V - buy_volume 3. Compute order-flow imbalance: OI_i = |buy_volume_i - sell_volume_i| 4. VPIN = Σ(OI_i) / (n × V) (n=50-bucket rolling window) Interpretation: VPIN < 0.3 -> normal, low informed-trading share VPIN 0.3-0.5 -> caution, informed trading rising VPIN > 0.5 -> dangerous, high probability that major information is about to be released China A-share usage: A sudden VPIN spike in a stock may foreshadow: - insider trading ahead of a major announcement - institutional position building / distribution Before the 2015 China A-share flash crashes, VPIN stayed above 0.6 for a prolonged period ``` **Kyle's Lambda (price impact coefficient)**: ``` Model: ΔP = λ × OrderFlow + ε where OrderFlow = buy volume - sell volume Estimation method: 1. Compute ΔP and OrderFlow in 5-minute windows 2. Regress ΔP = α + λ × OrderFlow 3. λ = price change caused by one unit of order flow Interpretation: Large λ -> poor liquidity, high impact Small λ -> good liquidity, large orders can be executed cheaply Typical China A-share values: Large cap (CSI 300): λ ≈ 0.001-0.005 Mid cap (CSI 500): λ ≈ 0.005-0.02 Small cap (CSI 1000): λ ≈ 0.02-0.1 ``` ### Liquidity Measures | Metric | Formula | Advantages | Disadvantages | |------|------|------|------| | Amihud illiquidity | `|R_t| / Volume_t` | Requires only daily data | Sensitive to extreme returns | | Roll implied spread | `2√(-Cov(R_t, R_{t-1}))` | Requires only daily data | Fails when covariance is positive | | LOT zero-return ratio | zero-return days / total days | Intuitive | Too coarse | | Turnover ratio | volume / free float | Simple and intuitive | Does not reflect price impact | | Traded value | average daily notional | Absolute liquidity | Does not reflect relative impact | ``` Amihud calculation (China A-shares): ILLIQ = (1/D) × Σ(|R_d| / VOL_d) (D=trading days, monthly) Normalization: ILLIQ × 10^6 (for readability) Screening rules: ILLIQ < 0.5 -> high liquidity (large-cap blue chips) ILLIQ 0.5-5 -> medium liquidity ILLIQ > 5 -> low liquidity (trade cautiously) Strategy application: - Liquidity factor: low-liquidity stocks tend to earn long-run excess return (liquidity premium) - Liquidity monitor: sudden rise in ILLIQ -> warning of liquidity drying up ``` ## Analysis Framework ### 1. Price-Impact Models **Power-law impact**: > **Naming.** This is the concave impact term from the Almgren-Chriss > literature, and it is neither linear (the exponent is 0.6, not 1) nor > Almgren-Chriss *optimal execution* — no trading trajectory, no > permanent/temporary split and no risk-aversion parameter is computed here or > anywhere in this repo. The tested implementation is > `src.quantlib.impact.sqrt_impact`; call it rather than retyping the formula. ``` Model: impact = η × σ × (Q / V)^0.6 η: impact coefficient, about 0.5-1.5 for China A-shares σ: daily volatility Q: traded quantity (shares) V: average daily volume (shares) Example: Sell 100,000 shares of Kweichow Moutai Average daily volume 5,000,000 shares, daily volatility 1.8% impact = 1.0 × 0.018 × (100000/5000000)^0.6 = 0.018 × 0.0085 = 0.015% (1.5bp, acceptable) Sell 100,000 shares of a small-cap stock Average daily volume 500,000 shares, daily volatility 3.0% impact = 1.0 × 0.03 × (100000/500000)^0.6 = 0.03 × 0.076 = 0.23% (23bp, should be executed in slices) Execution-splitting methods: TWAP: uniform in clock time -> simple but ignores market state VWAP: volume-profile execution -> better matches market rhythm IS: minimize Implementation Shortfall -> optimal but requires real-time optimization ``` **Nonlinear impact (square-root model)**: ``` impact = σ × √(Q / (ADV × T)) σ: daily volatility Q: total trade size ADV: average daily traded value T: execution days Applicable to: large trades (Q/ADV > 5%) ``` ### 2. Limit Order Book Analysis ``` Depth metrics: Level 1 depth: queue size at the best bid and best ask Level 5 depth: total queue size across the first 5 levels Depth asymmetry: (Bid depth - Ask depth) / (Bid depth + Ask depth) > 0 -> stronger bid side, price tends to rise < 0 -> stronger ask side, price tends to fall Resilience: The speed at which the book recovers after a large-order impact Fast recovery -> good liquidity, temporary impact Slow recovery -> poor liquidity, persistent impact China A-share LOB characteristics: - The shallowest depth is in the 15 minutes before the open (highest information asymmetry) - Depth improves from 10:00-10:30 (institutions begin participating) - Best depth is from 14:00-14:57 (most intraday information has been digested) - During the 14:57-15:00 closing auction, depth changes sharply (late-day grabbing / dumping) Order-book imbalance signal: OIR = (Bid_vol - Ask_vol) / (Bid_vol + Ask_vol) Rolling 5-minute OIR > 0.3 -> short-term bullish signal (accuracy about 55-60%) Note: in China A-shares, large orders are often rapidly added and canceled (icebergs / spoofing), so OIR signals need filtering ``` ### 3. Flash-Crash Mechanism and Prevention ``` Flash-crash characteristics: 1. Price drops more than 5% within minutes 2. Volume first expands, then collapses (liquidity evaporates) 3. Bid-ask spread widens sharply (market makers pull quotes) 4. Followed by a V-shaped rebound (not always fully recovered) Triggers: - Large market order + thin liquidity -> punches through multiple levels instantly - Stop-loss chain -> initial selloff triggers more stop orders - Algo resonance -> multiple trend-following algos sell simultaneously - ETF discount arbitrage -> ETF redemption and constituent selling intensify the drop Preventive measures: 1. Use limit orders instead of market orders: specify the maximum acceptable price 2. Monitor VPIN: if VPIN breaks above 0.5 -> stop trading 3. Liquidity threshold: exclude instruments with Amihud > 10 4. Spread monitor: if spread widens suddenly to >5x normal -> pause orders 5. Time avoidance: do not execute large orders in the first 15 minutes after open or the last 5 minutes before close China A-share flash-crash cases: 2015 Jun-Jul: thousands of stocks hit limit-down, with VPIN staying elevated 2020-07-13: Shanghai Composite plunged and then rebounded in a V-shape Pattern: liquidity dries up -> limit-down locking (China-specific) -> next-day panic selling ``` ### 4. China A-Share-Specific Microstructure ``` Call-auction strategy: 9:15-9:20: orders can be entered and canceled, mostly probing quotes (low reference value) 9:20-9:25: orders can be entered but not canceled, so real intent is revealed Signal: after 9:20, buy orders far exceed sell orders -> likely gap-up open Execution: place orders at 9:24:50 (last 10 seconds of the call auction) Risk: cannot cancel, and the final execution price may deviate from expectation Closing call auction (14:57-15:00): Feature: closing price is decided within 3 minutes, with concentrated institutional rebalancing and index-fund flows Signal: closing-auction volume > 10% of the whole day -> institutions are rebalancing Strategy application: - VWAP algos should finish most of execution before 14:50, leaving a small residual for the close - Avoid placing large orders after 14:57 (high price uncertainty) Block-trade discount signal: Discount = (block-trade price - closing price) / closing price Discount < -5%: seller is eager to exit -> short-term bearish Discount > -2%: traded near market price -> may be turnover rather than reduction Buyer identity: Well-known institutional seat buys -> positive signal Same broker on both sides -> may be wash trading (neutral) ``` ## Output Format Microstructure analysis report: ``` === Liquidity Diagnosis === Instrument: 000858.SZ Wuliangye Date: 2026-03-28 Average daily traded value: 2.8 billion RMB Turnover ratio: 0.85% Amihud: 0.32 (high liquidity) Effective spread: 0.05% (2.5bp) Kyle Lambda: 0.003 === Order-Flow Analysis === VPIN: 0.28 (normal) Order-book imbalance (OIR): +0.12 (mild bid-side bias) Net large-order buying: +230 million RMB (institutional buying bias) === Trading-Cost Estimate === Planned trade size: 500,000 shares (about 40 million RMB) Estimated impact cost: 0.08% (32k RMB) Commission: 0.025% (10k RMB) Stamp duty: 0.05% (20k RMB, sell side) Total one-way transaction cost: about 0.16% === Execution Suggestion === Recommended strategy: VWAP Execution window: 10:00-14:50 (avoid the open and the close) Number of slices: 5-8 (about 60k-100k shares per slice) Time sensitivity: low (VPIN is normal, no urgency to execute) ``` ## Notes 1. **Data requirement is high**: microstructure analysis requires tick-level / Level-2 data, while ordinary daily data only supports rough measures such as Amihud / Roll 2. **China A-share Level-2 data**: ten-level depth data from SSE / SZSE requires a paid subscription, costing roughly 50k-200k RMB per year 3. **High-frequency trading restrictions**: China A-shares strictly prohibit programmatic quote-cancel manipulation (`spoofing`), so microstructure signals are for analysis only, not for HFT strategies 4. **VPIN calibration**: bucket size has a large impact on results and must be adjusted for instrument liquidity; one parameter does not fit all 5. **Cross-market differences**: China A-share `T+1` settlement and daily price limits make its microstructure significantly different from textbook US-equity models 6. **Illusion of liquidity**: high turnover in some China A-shares comes from speculative matched trading and does not represent true liquidity ## Dependencies ```bash pip install pandas numpy scipy ```