آرڈر بک ڈیٹا سے Lee-Ready کی ٹریڈ سمت کی توثیق

# Lee-Ready Trade Classification Validation


# Lee-Ready Trade Classification Validation

**Chapter 3: Market Microstructure**

**Docker image**: `ml4t`

## Purpose

Quantify how well the Lee-Ready algorithm recovers the aggressor side of
trades, using DataBento XNAS-ITCH MBO data (which carries the ground-truth
aggressor flag) as the benchmark.

## Learning Objectives

After completing this notebook, you will be able to:
- Reconstruct the LOB from DataBento MBO messages and align trades to the
  contemporaneous quote midpoint.
- Apply the Lee-Ready quote-test + tick-test cascade and compare its
  accuracy against the tick test alone.
- Read the per-day breakdown (NVDA, 5 trading days) and reproduce the ~16pp
  gap §3.4 reports between tick-only and Lee-Ready classification.

## Book reference

Section §3.4, *The Art of Sampling* — Lee-Ready subsection (Table 3.3).

## Prerequisites

- DataBento XNAS-ITCH MBO parquets at
  `data/equities/market/microstructure/market_by_order/{SYMBOL}/`
  (downloaded via `data/equities/market/microstructure/mbo_download.py`).

## Decision Time vs Exchange Time

This validation uses **exchange timestamps** for trade-quote alignment, which
matches the ground truth's timestamping. In live trading, observation delay
means the quote you could actually have seen at decision time lags the
exchange state — apply a conservative lag (~1ms co-located, ~10ms retail)
in any backtest that relies on this kind of classification.

---

## Setup

```python
"""Lee-Ready Trade Classification Validation — validate Lee-Ready against DataBento ground truth aggressor labels."""

from collections import Counter
from pathlib import Path

import matplotlib.pyplot as plt
import polars as pl

# Import loader for MBO data
from data import load_mbo_data

# ML4T imports - path resolution
from utils.paths import get_output_dir
from utils.style import COLORS, show_with_alt
```

```python
SYMBOL = "NVDA"
MAX_ROWS = 0  # 0 = all rows per file
MAX_VALIDATION_DAYS = 5  # Number of days for multi-day validation
```

```python
# Normalize MAX_ROWS: 0 means no limit
if MAX_ROWS == 0:
    MAX_ROWS = None
# Get file paths from the canonical loader (handles legacy/new path resolution)
data_files = load_mbo_data(symbols=[SYMBOL], list_files=True)
SYMBOL_DIR = data_files[0].parent if data_files else None
OUTPUT_DIR = get_output_dir(3, "algoseek")

# Check data availability (files already loaded via load_mbo_data)
print(f"Symbol: {SYMBOL}")
print(f"Data files: {len(data_files)}")
if data_files:
    print(f"First file: {data_files[0].name}")
```

## 1. Load DataBento MBO Data

DataBento MBO format:
- `action`: A(Add), C(Cancel), F(Fill), M(Modify), T(Trade), R(Clear)
- `side`: B(Bid/Buy), A(Ask/Sell), N(None)
- `price`: Integer fixed-point (nanodollars, divide by 1e9)
- `size`: Order/trade size
- `order_id`: Unique order reference
- `timestamp`: Exchange timestamp

```python
def load_databento_mbo(file_path: Path, max_rows: int | None = None) -> pl.DataFrame:
    """Load and normalize DataBento MBO data.

    Handles both file layouts the repo can produce: Download Center files carry a
    ``timestamp`` column, while the API downloader (``mbo_download.py``) carries
    ``ts_event``. We normalize to ``timestamp`` and filter regular trading hours in
    exchange-local time so the window is correct on either side of a DST change.
    """
    df = pl.read_parquet(file_path)

    # Apply row limit
    if max_rows is not None:
        df = df.head(max_rows)

    # Normalize the event-time column name (API files use `ts_event`).
    if "timestamp" not in df.columns and "ts_event" in df.columns:
        df = df.rename({"ts_event": "timestamp"})

    # DataBento timestamps are UTC; keep a UTC-naive column for downstream code.
    df = df.with_columns(pl.col("timestamp").cast(pl.Datetime("ns")))

    # Convert fixed-point prices to dollars if needed
    if "price" in df.columns and df["price"].max() > 1_000_000:
        df = df.with_columns((pl.col("price") / 1e9).alias("price"))

    # Regular trading hours are defined on the exchange's clock, so convert before
    # filtering: a window fixed in UTC is an hour wrong for half the year.
    _et = pl.col("timestamp").dt.replace_time_zone("UTC").dt.convert_time_zone("America/New_York")
    df = df.filter(
        ((_et.dt.hour() > 9) | ((_et.dt.hour() == 9) & (_et.dt.minute() >= 30)))
        & (_et.dt.hour() < 16)
    )

    # Trades sharing a timestamp have no guaranteed order after this sort, which matters
    # only for the tick test's notion of 'the previous trade'.
    return df.sort("timestamp")
```

```python
# Load one day for validation
sample_df = None
if data_files:
    sample_df = load_databento_mbo(data_files[0], max_rows=MAX_ROWS)
    print(f"Loaded {len(sample_df):,} messages")

    # Action distribution
    action_counts = sample_df.group_by("action").len().sort("len", descending=True)
    print("\nAction distribution:")
    for row in action_counts.iter_rows():
        print(f"  {row[0]}: {row[1]:,}")
```

## 2. Lee-Ready Classification with LOB Reconstruction

Lee-Ready algorithm (1991):
1. **Quote test**: Compare trade price to midpoint
   - Above midpoint → buy-initiated
   - Below midpoint → sell-initiated
2. **Tick test** (fallback when at midpoint):
   - Higher than previous trade → buy
   - Lower than previous trade → sell

Key implementation detail: We must maintain LOB state as we process trades
to get the correct midpoint at each trade time.

```python
def _update_book(
    action: str,
    side: str,
    price: float,
    size: int,
    order_id: int,
    book: dict[str, Counter],
    order_registry: dict[int, dict],
) -> dict[str, Counter]:
    """Apply a book-affecting action (R/A/M/C/F) to the LOB state."""
    if action == "R":  # Clear book
        book = {"B": Counter(), "A": Counter()}
        order_registry.clear()

    elif action == "A":  # Add order
        order_registry[order_id] = {"side": side, "price": price, "size": size}
        book[side][price] += size

    elif action == "M":  # Modify order
        if order_id in order_registry:
            old = order_registry[order_id]
            book[old["side"]][old["price"]] -= old["size"]
            if book[old["side"]][old["price"]] <= 0:
                del book[old["side"]][old["price"]]
            order_registry[order_id] = {"side": side, "price": price, "size": size}
            book[side][price] += size

    elif action in ("C", "F"):  # Cancel or Fill
        if order_id in order_registry:
            reg = order_registry[order_id]
            book[reg["side"]][reg["price"]] -= size
            if book[reg["side"]][reg["price"]] <= 0:
                del book[reg["side"]][reg["price"]]
            reg["size"] -= size
            if reg["size"] <= 0:
                del order_registry[order_id]

    return book
```

### Apply Lee-Ready Classification
Quote test with tick test fallback for trade direction inference.

```python
def _apply_lee_ready(
    price: float,
    book: dict[str, Counter],
    last_price: float | None,
    last_tick_dir: int,
) -> tuple[int, int]:
    """Apply Lee-Ready quote test + tick test fallback. Returns (classification, updated_tick_dir)."""
    if book["B"] and book["A"]:
        best_bid = max(book["B"].keys())
        best_ask = min(book["A"].keys())
        midpoint = (best_bid + best_ask) / 2

        # Quote test
        if price > midpoint:
            lee_ready = 1  # Buy
        elif price < midpoint:
            lee_ready = -1  # Sell
        else:
            # At midpoint - use tick test
            if last_price is not None:
                if price > last_price:
                    last_tick_dir = 1
                elif price < last_price:
                    last_tick_dir = -1
            lee_ready = last_tick_dir
    else:
        # No book - use tick test only
        if last_price is not None:
            if price > last_price:
                lee_ready = 1
            elif price < last_price:
                lee_ready = -1
            else:
                lee_ready = last_tick_dir
        else:
            lee_ready = 0

    return lee_ready, last_tick_dir
```

### Classify Trades via Lee-Ready
Walk through MBO messages, maintain book state, and classify each trade.

```python
def classify_trades_lee_ready_databento(
    df: pl.DataFrame, show_progress: bool = True
) -> pl.DataFrame:
    """
    Classify trade direction using Lee-Ready on DataBento MBO data.

    Maintains LOB state while processing to get accurate midpoint at trade time.

    Parameters
    ----------
    df : pl.DataFrame
        DataBento MBO data with action, side, price, size, order_id, timestamp
    show_progress : bool
        Whether to show progress bar

    Returns
    -------
    pl.DataFrame
        Trades with columns: timestamp, price, size, ground_truth, lee_ready, correct
    """
    order_registry: dict[int, dict] = {}
    book: dict[str, Counter] = {"B": Counter(), "A": Counter()}
    classified_trades = []
    last_price = None
    last_tick_dir = 0

    cols_df = df.select(["timestamp", "action", "side", "price", "size", "order_id"])
    if show_progress:
        print(f"Processing {len(cols_df):,} messages...")

    for row in cols_df.iter_rows(named=True):
        action, side, price, size = row["action"], row["side"], row["price"], row["size"]
        order_id, ts = row["order_id"], row["timestamp"]

        if side == "N" and action != "T":
            continue

        if action in ("R", "A", "M", "C", "F"):
            book = _update_book(action, side, price, size, order_id, book, order_registry)

        elif action == "T":
            # Get ground truth (DataBento provides aggressor side)
            ground_truth = 1 if side == "B" else (-1 if side == "A" else 0)
            if ground_truth == 0:
                continue

            lee_ready, last_tick_dir = _apply_lee_ready(price, book, last_price, last_tick_dir)
            classified_trades.append(
                {
                    "timestamp": ts,
                    "price": price,
                    "size": size,
                    "ground_truth": ground_truth,
                    "lee_ready": lee_ready,
                    "correct": int(ground_truth == lee_ready),
                }
            )
            last_price = price

    print(f"Classified {len(classified_trades):,} trades")
    return pl.DataFrame(classified_trades)
```

## 3. Run Validation

```python
results = None
if sample_df is not None and len(sample_df) > 1000:
    results = classify_trades_lee_ready_databento(sample_df)

    if len(results) > 0:
        # Compute accuracy
        accuracy = results["correct"].mean() * 100

        # Breakdown by ground truth
        buy_trades = results.filter(pl.col("ground_truth") == 1)
        sell_trades = results.filter(pl.col("ground_truth") == -1)

        buy_accuracy = buy_trades["correct"].mean() * 100 if len(buy_trades) > 0 else 0
        sell_accuracy = sell_trades["correct"].mean() * 100 if len(sell_trades) > 0 else 0

        print("\n" + "=" * 50)
        print("LEE-READY VALIDATION RESULTS")
        print("=" * 50)
        print(f"\nTotal trades classified: {len(results):,}")
        print(f"Overall accuracy: {accuracy:.2f}%")
        print("\nBy ground truth:")
        print(f"  Buy-initiated:  {buy_accuracy:.2f}% ({len(buy_trades):,} trades)")
        print(f"  Sell-initiated: {sell_accuracy:.2f}% ({len(sell_trades):,} trades)")

        # Confusion matrix
        true_pos_buy = results.filter(
            (pl.col("ground_truth") == 1) & (pl.col("lee_ready") == 1)
        ).height
        false_neg_buy = results.filter(
            (pl.col("ground_truth") == 1) & (pl.col("lee_ready") != 1)
        ).height
        true_pos_sell = results.filter(
            (pl.col("ground_truth") == -1) & (pl.col("lee_ready") == -1)
        ).height
        false_neg_sell = results.filter(
            (pl.col("ground_truth") == -1) & (pl.col("lee_ready") != -1)
        ).height

        print("\nConfusion matrix:")
        print(f"  True Buy  (GT=B, LR=B): {true_pos_buy:,}")
        print(f"  False Buy (GT=B, LR≠B): {false_neg_buy:,}")
        print(f"  True Sell (GT=A, LR=A): {true_pos_sell:,}")
        print(f"  False Sell(GT=A, LR≠A): {false_neg_sell:,}")
```

## 4. Multi-Day Validation

Run validation across multiple days to get robust statistics.

```python
def validate_multiple_days(
    data_files: list,
    max_files: int | None = None,
    show_progress: bool = True,
    max_rows_per_file: int | None = None,
) -> pl.DataFrame:
    """Run Lee-Ready validation across multiple days."""
    all_results = []

    files_to_process = data_files[:max_files] if max_files else data_files

    rows_limit = max_rows_per_file

    for i, file in enumerate(files_to_process):
        print(f"Processing day {i + 1}/{len(files_to_process)}: {file.name}")
        try:
            df = load_databento_mbo(file, max_rows=rows_limit)
            if len(df) > 1000:
                day_results = classify_trades_lee_ready_databento(df, show_progress=False)
                if len(day_results) > 0:
                    date_str = file.stem.split("-")[-1].split(".")[0]
                    day_results = day_results.with_columns(pl.lit(date_str).alias("timestamp"))
                    all_results.append(day_results)
        except Exception as e:
            print(f"Error processing {file.name}: {e}")

    if all_results:
        return pl.concat(all_results)
    return pl.DataFrame()
```

```python
multi_day_results = None
if data_files:
    multi_day_results = validate_multiple_days(
        data_files, max_files=MAX_VALIDATION_DAYS, max_rows_per_file=MAX_ROWS
    )

    if multi_day_results is not None and len(multi_day_results) > 0:
        print("\n" + "=" * 50)
        print("MULTI-DAY VALIDATION SUMMARY")
        print("=" * 50)

        # Overall accuracy
        overall_accuracy = multi_day_results["correct"].mean() * 100
        print(f"\nTotal trades: {len(multi_day_results):,}")
        print(f"Overall accuracy: {overall_accuracy:.2f}%")

        # By day
        daily_stats = (
            multi_day_results.group_by("timestamp")
            .agg(
                [
                    pl.len().alias("trades"),
                    (pl.col("correct").sum() / pl.len() * 100).alias("accuracy"),
                ]
            )
            .sort("timestamp")
        )
        print("\nDaily breakdown:")
        for row in daily_stats.iter_rows():
            print(f"  {row[0]}: {row[2]:.2f}% ({row[1]:,} trades)")
```

## 5. Compare to Tick Test Only

Compare Lee-Ready (quote + tick test) vs tick test alone.

```python
def classify_tick_test_only(df: pl.DataFrame, show_progress: bool = True) -> pl.DataFrame:
    """Classify trades using tick test only (no quote test)."""
    trades = df.filter(pl.col("action") == "T").filter(pl.col("side").is_in(["B", "A"]))

    if len(trades) == 0:
        return pl.DataFrame()

    # Add ground truth
    trades = trades.with_columns(
        pl.when(pl.col("side") == "B")
        .then(1)
        .when(pl.col("side") == "A")
        .then(-1)
        .otherwise(0)
        .alias("ground_truth")
    )

    # Tick test: compare to previous price
    trades = trades.with_columns(
        pl.when(pl.col("price") > pl.col("price").shift(1))
        .then(1)
        .when(pl.col("price") < pl.col("price").shift(1))
        .then(-1)
        .otherwise(0)
        .alias("tick_test")
    )

    # Zero-tick handling: use last non-zero direction
    classified = []
    last_dir = 0
    for row in trades.iter_rows(named=True):
        if row["tick_test"] != 0:
            last_dir = row["tick_test"]
        pred = last_dir if row["tick_test"] == 0 else row["tick_test"]
        correct = 1 if pred == row["ground_truth"] else 0
        classified.append(
            {
                "timestamp": row["timestamp"],
                "ground_truth": row["ground_truth"],
                "tick_test": pred,
                "correct": correct,
            }
        )

    return pl.DataFrame(classified)
```

```python
if sample_df is not None and len(sample_df) > 1000:
    tick_results = classify_tick_test_only(sample_df)

    if len(tick_results) > 0:
        tick_accuracy = tick_results["correct"].mean() * 100

        print("\n" + "=" * 50)
        print("TICK TEST ONLY RESULTS")
        print("=" * 50)
        print(f"Trades: {len(tick_results):,}")
        print(f"Accuracy: {tick_accuracy:.2f}%")

        # Compare to Lee-Ready
        if results is not None and len(results) > 0:
            lr_accuracy = results["correct"].mean() * 100
            print("\nComparison:")
            print(f"  Lee-Ready:  {lr_accuracy:.2f}%")
            print(f"  Tick only:  {tick_accuracy:.2f}%")
            print(f"  Improvement: {lr_accuracy - tick_accuracy:.2f}%")
```

## 6. Multi-Day Classification Accuracy (Table 3.3)

Accuracy is aggregated over the same multi-day window the book's Table 3.3 reports.

The tick test is scored two ways, because it faces a choice the quote test does not.
Many trades print at the same price as the one before them, and the rule has nothing
to read. The **continuous** cohort carries the last non-zero direction forward, so it
classifies every trade; the **non-zero** cohort declines to classify those trades at
all, so it is scored only on the ones whose price moved. Comparing the two separates
how good the rule is from how often it has anything to go on.

The summary is written to parquet so the book's table script can rebuild Table 3.3
without re-running this notebook.

```python
def tick_test_cohorts(df: pl.DataFrame) -> dict:
    """Return continuous and non-zero tick-test accuracy/coverage for one day."""
    trades = df.filter(pl.col("action") == "T").filter(pl.col("side").is_in(["B", "A"]))
    if len(trades) == 0:
        return {"n_trades": 0, "continuous_correct": 0, "nonzero_n": 0, "nonzero_correct": 0}

    # Enforce per-day chronological order locally so the price.shift(1)
    # tick-test does not rely on upstream sort invariants.
    trades = trades.sort("timestamp")

    trades = trades.with_columns(
        pl.when(pl.col("side") == "B")
        .then(1)
        .when(pl.col("side") == "A")
        .then(-1)
        .otherwise(0)
        .alias("ground_truth"),
        pl.when(pl.col("price") > pl.col("price").shift(1))
        .then(1)
        .when(pl.col("price") < pl.col("price").shift(1))
        .then(-1)
        .otherwise(0)
        .alias("raw_tick"),
    )
    # Continuous: zero-tick rows carry forward last non-zero direction.
    trades = trades.with_columns(
        pl.when(pl.col("raw_tick") == 0)
        .then(None)
        .otherwise(pl.col("raw_tick"))
        .forward_fill()
        .fill_null(0)
        .alias("continuous_tick")
    )
    n_trades = len(trades)
    continuous_correct = (trades["continuous_tick"] == trades["ground_truth"]).sum()
    nonzero = trades.filter(pl.col("raw_tick") != 0)
    nonzero_n = len(nonzero)
    nonzero_correct = (nonzero["raw_tick"] == nonzero["ground_truth"]).sum() if nonzero_n > 0 else 0
    return {
        "n_trades": n_trades,
        "continuous_correct": int(continuous_correct),
        "nonzero_n": int(nonzero_n),
        "nonzero_correct": int(nonzero_correct),
    }
```

```python
multi_day_tick_summary = None
if data_files:
    per_day_rows = []
    for i, file in enumerate(data_files[:MAX_VALIDATION_DAYS]):
        print(f"Tick-test day {i + 1}/{MAX_VALIDATION_DAYS}: {file.name}")
        day_df = load_databento_mbo(file, max_rows=MAX_ROWS)
        date_str = file.stem.split("-")[-1].split(".")[0]
        agg = tick_test_cohorts(day_df)
        agg["date"] = date_str
        per_day_rows.append(agg)

    per_day = pl.DataFrame(per_day_rows)
    total_trades = int(per_day["n_trades"].sum())
    total_cont_correct = int(per_day["continuous_correct"].sum())
    total_nonzero_n = int(per_day["nonzero_n"].sum())
    total_nonzero_correct = int(per_day["nonzero_correct"].sum())

    continuous_accuracy = 100.0 * total_cont_correct / max(total_trades, 1)
    nonzero_coverage = 100.0 * total_nonzero_n / max(total_trades, 1)
    nonzero_accuracy = 100.0 * total_nonzero_correct / max(total_nonzero_n, 1)

    lr_total_accuracy = (
        100.0 * float(multi_day_results["correct"].mean())
        if multi_day_results is not None and len(multi_day_results) > 0
        else float("nan")
    )
    lr_total_trades = (
        int(len(multi_day_results))
        if multi_day_results is not None and len(multi_day_results) > 0
        else 0
    )

    print("\n" + "=" * 60)
    print("TABLE 3.3 — CLASSIFICATION ACCURACY (5-day NVDA aggregate)")
    print("=" * 60)
    print(f"Total trades classified: {total_trades:,}")
    print(f"\n{'Method':<24} {'Coverage':>10} {'Accuracy':>10}")
    print(f"{'Lee-Ready (quote+tick)':<24} {'100%':>10} {f'{lr_total_accuracy:.1f}%':>10}")
    print(f"{'Tick test (continuous)':<24} {'100%':>10} {f'{continuous_accuracy:.1f}%':>10}")
    print(
        f"{'Tick test (non-zero)':<24} {f'{nonzero_coverage:.0f}%':>10} {f'{nonzero_accuracy:.1f}%':>10}"
    )

    # Persist Table 3.3 summary for book-side script regeneration.
    output_dir = get_output_dir(3, "databento")
    output_dir.mkdir(parents=True, exist_ok=True)
    summary_path = output_dir / "table_3_3_classification_accuracy.parquet"
    multi_day_tick_summary = pl.DataFrame(
        [
            {
                "method": "Lee-Ready (quote+tick)",
                "coverage_pct": 100.0,
                "accuracy_pct": lr_total_accuracy,
                "n_trades": lr_total_trades,
            },
            {
                "method": "Tick test (continuous)",
                "coverage_pct": 100.0,
                "accuracy_pct": continuous_accuracy,
                "n_trades": total_trades,
            },
            {
                "method": "Tick test (non-zero)",
                "coverage_pct": nonzero_coverage,
                "accuracy_pct": nonzero_accuracy,
                "n_trades": total_nonzero_n,
            },
        ]
    )
    multi_day_tick_summary.write_parquet(summary_path)
    print(f"\nSaved: {summary_path}")
```

### Table 3.3 as a chart

Read the two numbers on each bar together. The bar length is accuracy on the trades a
method classified; the label to its right is what share of trades that was. A method
that only answers when the answer is easy scores well on a small denominator, and the
non-zero tick test is exactly that case - which is why coverage is printed beside
accuracy rather than left out of the comparison.

```python
if "multi_day_tick_summary" in globals():
    _s = multi_day_tick_summary.sort("accuracy_pct")
    _methods = _s["method"].to_list()
    _acc = _s["accuracy_pct"].to_list()
    _cov = _s["coverage_pct"].to_list()

    fig, ax = plt.subplots(figsize=(10, 4))
    bars = ax.barh(_methods, _acc, color=COLORS["blue"], height=0.6)
    for bar, acc, cov in zip(bars, _acc, _cov):
        y = bar.get_y() + bar.get_height() / 2
        ax.text(
            acc - 1.0,
            y,
            f"{acc:.1f}%",
            va="center",
            ha="right",
            color=COLORS["silver"],
            fontweight="bold",
        )
        ax.text(
            101,
            y,
            f"{cov:.0f}% coverage",
            va="center",
            ha="left",
            color=COLORS["neutral"],
            fontsize=9,
        )
    ax.set_xlim(0, 120)
    ax.set_xlabel("Accuracy vs DataBento aggressor labels (%)")
    ax.set_title(
        "Classification accuracy against the venue's aggressor labels, with coverage",
        loc="left",
    )
    ax.spines[["top", "right"]].set_visible(False)
    show_with_alt(
        fig,
        "A horizontal bar chart with one bar per classification method, sorted with the shortest at the bottom. Each bar's length is its accuracy against the venue's aggressor labels as a percentage, labelled inside the bar, with the share of trades that method classified printed as a separate label to the right of it.",
    )
```

## Key Takeaways

### What the comparison establishes

Three things are being compared and it is worth being precise about what each is. The
venue's own aggressor label is the record of which side crossed, and it is the standard
the other two are scored against rather than a method with an accuracy of its own.
Lee-Ready reads the trade price against the quote midpoint and falls back on the tick
test where that is uninformative. The tick test alone reads only the direction of the
last price change.

The figure above carries the numbers. What they show is how much of the classification
comes from the quote: the tick test is the part of Lee-Ready that runs when the quote
says nothing, and scoring it alone measures what the quote was contributing.

### Why Lee-Ready Works

The quote test (comparing trade price to midpoint) captures the fundamental
market microstructure: buyer-initiated trades tend to occur at or above the ask
(above midpoint), while seller-initiated trades occur at or below the bid.

### Implementation Notes

1. **LOB state matters**: Must maintain accurate book state at each trade
2. **Tick test fallback**: Only used when trade exactly at midpoint
3. **Zero-tick handling**: Preserve last direction on unchanged price

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**Reference**: Lee & Ready (1991), "Inferring Trade Direction from Intraday Data"
![notebook output](figures/p1_1.png)