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VPIN (Volume-synchronized Probability of Informed Trading)

VPIN measures the probability that informed traders are active in the market by looking at order flow imbalance across fixed-volume buckets. Unlike time-based metrics, VPIN synchronizes to volume — each bucket contains the same total traded quantity, so high-activity and low-activity periods are weighted equally.

Problem​

You want to detect when informed traders are likely active. Time-based imbalance metrics can be noisy — a 1-minute window during a quiet period captures very different market dynamics than a 1-minute window during a news event. VPIN normalizes by volume instead of time, giving a more consistent signal.

Solution​

Split the trade stream into fixed-volume buckets, compute the buy/sell imbalance within each bucket, then take a rolling average over the last N buckets:

VPIN — volume-synchronized informed trading probabilityDemo this query
WITH bucketed AS (
    SELECT
        t.timestamp,
        t.symbol,
        t.side,
        t.price,
        t.quantity,
        floor(
            sum(t.quantity) OVER (PARTITION BY symbol ORDER BY timestamp)
            / 1000000
        ) AS vol_bucket
    FROM fx_trades t
    WHERE t.symbol = 'EURUSD'
        AND t.timestamp IN '$yesterday'
),
bucket_stats AS (
    SELECT
        symbol,
        vol_bucket,
        min(timestamp) AS bucket_start,
        max(timestamp) AS bucket_end,
        count() AS trade_count,
        sum(quantity) AS total_vol,
        sum(CASE WHEN side = 'buy' THEN quantity ELSE 0.0 END) AS buy_vol,
        sum(CASE WHEN side = 'sell' THEN quantity ELSE 0.0 END) AS sell_vol,
        abs(
            sum(CASE WHEN side = 'buy' THEN quantity ELSE 0.0 END)
            - sum(CASE WHEN side = 'sell' THEN quantity ELSE 0.0 END)
        ) / sum(quantity) AS bucket_imbalance
    FROM bucketed
    GROUP BY symbol, vol_bucket
)
SELECT
    symbol,
    vol_bucket,
    bucket_start,
    bucket_end,
    total_vol,
    buy_vol,
    sell_vol,
    bucket_imbalance,
    avg(bucket_imbalance) OVER (
        PARTITION BY symbol
        ORDER BY vol_bucket
        ROWS BETWEEN 49 PRECEDING AND CURRENT ROW
    ) AS vpin
FROM bucket_stats
ORDER BY vol_bucket;

How it works​

Step 1 — Volume bucketing​

The first CTE assigns a vol_bucket ID to each trade using a cumulative volume sum divided by the bucket size (1,000,000 units). All trades within the same bucket share the same ID. This is the key difference from time-based analysis — each bucket represents the same amount of market activity regardless of how long it took.

Step 2 — Bucket imbalance​

For each bucket, compute the absolute imbalance between buy and sell volume as a fraction of total volume. A bucket where 90% of the volume was buy-initiated has an imbalance of 0.8 (|0.9 − 0.1|). A perfectly balanced bucket has imbalance 0.0.

Step 3 — Rolling VPIN​

Average the bucket imbalance over a rolling window of 50 buckets. This is the VPIN estimate. The window size controls the trade-off between responsiveness and noise — fewer buckets react faster but are noisier.

Interpreting results​

VPIN ranges from 0 to 1:

  • VPIN near 0: Order flow is balanced — roughly equal buying and selling. Low probability of informed trading.
  • VPIN near 0.5: Moderate imbalance. Normal for trending markets.
  • VPIN above 0.7: Heavily one-sided flow. Informed traders are likely dominating. This is the danger zone for market makers — consider widening quotes or reducing exposure.

Watch for VPIN spikes — sudden jumps from a stable baseline indicate a regime change, often preceding large price moves. The 2010 Flash Crash, for example, was preceded by elevated VPIN readings.

Tuning parameters​

  • Bucket size (1000000): Adjust per symbol to get a reasonable number of buckets per day. For major FX pairs with billions in daily volume, 1M per bucket is fine. For less liquid instruments, reduce the bucket size.
  • Rolling window (50 buckets): The original VPIN paper uses 50 buckets. Shorter windows (20–30) are more responsive but noisier. Longer windows (100+) give a smoother signal but lag.
  • Symbol filter: VPIN is computed per symbol. The WHERE t.symbol = 'EURUSD' filter ensures volume bucketing doesn't mix symbols. To compute VPIN for multiple symbols, remove the filter — the PARTITION BY symbol in the window function handles separation.

VPIN per ECN​

Partition by ECN to see which venues carry more informed flow. An ECN with consistently higher VPIN is attracting (or routing) more informed traders:

VPIN per ECNDemo this query
WITH bucketed AS (
    SELECT
        t.timestamp,
        t.symbol,
        t.ecn,
        t.side,
        t.quantity,
        floor(
            sum(t.quantity) OVER (PARTITION BY symbol, ecn ORDER BY timestamp)
            / 1000000
        ) AS vol_bucket
    FROM fx_trades t
    WHERE t.symbol = 'EURUSD'
        AND t.timestamp IN '$yesterday'
),
bucket_stats AS (
    SELECT
        symbol,
        ecn,
        vol_bucket,
        min(timestamp) AS bucket_start,
        max(timestamp) AS bucket_end,
        sum(quantity) AS total_vol,
        abs(
            sum(CASE WHEN side = 'buy' THEN quantity ELSE 0.0 END)
            - sum(CASE WHEN side = 'sell' THEN quantity ELSE 0.0 END)
        ) / sum(quantity) AS bucket_imbalance
    FROM bucketed
    GROUP BY symbol, ecn, vol_bucket
)
SELECT
    symbol,
    ecn,
    vol_bucket,
    bucket_start,
    bucket_end,
    total_vol,
    bucket_imbalance,
    avg(bucket_imbalance) OVER (
        PARTITION BY symbol, ecn
        ORDER BY vol_bucket
        ROWS BETWEEN 49 PRECEDING AND CURRENT ROW
    ) AS vpin
FROM bucket_stats
ORDER BY ecn, vol_bucket;

Compare VPIN time series across ECNs. An ECN that shows elevated VPIN while others stay flat is where informed flow is concentrated. Combine with the ECN scorecard to cross-reference against markout-based toxicity — the two signals should align. When they diverge (high VPIN but flat markouts), the imbalance may be from correlated retail flow rather than informed trading.

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