Actualización incremental de datos de mercado | Stratmill

# Incremental Updates: Keeping Market Data Fresh


# Incremental Updates: Keeping Market Data Fresh

**Docker image**: `ml4t`

**Purpose**: Walk through the update lifecycle — initial load, daily delta,
gap detection, and a small health dashboard — so the same flow that runs
in `data/download_all.py --update` is visible end-to-end.

**Learning objectives**:
1. Compare full-refresh and incremental update costs.
2. Drive a daily delta with `DataManager.update(..., fill_gaps=False)` and
   understand why the `fill_gaps=True` default is unsafe for OHLCV data.
3. Verify completeness with `GapDetector(exclude_weekends=True)`.
4. Render a 3-panel health dashboard (volume, freshness, issues).

**Book reference**: §2.4 (storing data) — operational counterpart to the
storage benchmarks in notebooks 20 and 21.

**Prerequisites**: ml4t-data installed; live network access for Yahoo Finance.

**Why incremental updates**: a full refresh re-fetches every row a symbol has ever had,
and an incremental run fetches the sessions since the last one. On a universe of a few
hundred symbols with a decade of history, the first is millions of rows and the second is
hundreds, every day. Section 1 measures the ratio on the demo universe.

## Setup

```python
"""Incremental Updates — Keeping market data fresh with daily delta fetches."""

import logging
import shutil
from datetime import datetime

import matplotlib.pyplot as plt
import polars as pl
import structlog

# Quiet ml4t-data's structured logger so the notebook focuses on demo output.
structlog.configure(
    wrapper_class=structlog.make_filtering_bound_logger(logging.WARNING),
)

from ml4t.data import DataManager
from ml4t.data.storage import HiveStorage
from ml4t.data.storage.backend import StorageConfig
from ml4t.data.update_manager import GapDetector
from ml4t.data.validation import OHLCVValidator

from utils.downloading import update_through_last_complete_bar
from utils.paths import display_path, get_output_dir
from utils.style import COLORS, show_with_alt

# Storage for this notebook's demos. Wipe any prior-run artifacts so the
# initial-load + update sequence is reproducible.
DEMO_DIR = get_output_dir(2, "incremental_updates")
if DEMO_DIR.exists():
    shutil.rmtree(DEMO_DIR)
DEMO_DIR.mkdir(parents=True, exist_ok=True)

print(f"Demo storage: {display_path(DEMO_DIR)}")
```

### Declared parameters

The freshness thresholds are the two that carry a judgment. A daily equity feed that has
not moved in more than a weekend is behind, and one that has not moved in more than a week
is broken; those are the two lines the dashboard draws and colours against.

```python
DEMO_SYMBOLS = ["AAPL", "MSFT", "GOOGL"]
FULL_HISTORY_START = "2023-01-01"
FULL_HISTORY_END = "2024-12-31"
DEMO_PROVIDER = "yahoo"
UPDATE_LOOKBACK_DAYS = 7

FRESH_DAYS = 3  # up to this many days behind is fresh
STALE_DAYS = 7  # beyond this the feed is not merely behind
MAX_RETURN_THRESHOLD = 0.5  # the validator's extreme-return cutoff
STORAGE_COMPRESSION = "zstd"
```

---

## 1. Full Refresh vs Incremental Update

Let's demonstrate the difference with a concrete example.

### Step 1: Initial Load (Full History)

```python
config = StorageConfig(base_path=DEMO_DIR / "updates_demo", compression=STORAGE_COMPRESSION)
storage = HiveStorage(config=config)
dm = DataManager(storage=storage)

symbols = DEMO_SYMBOLS

print("=== Initial Load (Full History) ===")
for symbol in symbols:
    dm.load(symbol, FULL_HISTORY_START, FULL_HISTORY_END, provider=DEMO_PROVIDER)
    meta = dm.get_metadata(symbol)
    print(f"  {symbol}: stored ({meta['row_count']} rows)")
```

### Step 2: Incremental Update (Only New Data)

An update reads the last timestamp in storage, refetches a small overlap
(`UPDATE_LOOKBACK_DAYS`) so a bar the vendor revised replaces the stored one, and
merges everything since.

**Where it stops matters more than where it starts.** Yahoo returns the
current exchange date as a row with accumulating volume and no
open/high/low/close. A bar with no prices is not a bar, and the provider says
so:

```
DataValidationError: yahoo: Column 'open' contains 1 null values
```

`DataManager.update()` fetches to `datetime.now()`, so it asks for that row on
every trading day and raises. `update_through_last_complete_bar` performs the
same delta and **finds** the end of its window rather than computing one. It
starts a day before the current exchange date — in exchange time, not UTC,
since at 22:00 in New York the UTC date is already tomorrow — and steps back a
day at a time while the provider refuses the window.

The retreat is what a date cannot do. The placeholder row usually resolves a
few hours after the close, and sometimes it does not: Yahoo's 2026-09-03 daily
bar for AAPL was still `NaN, NaN, NaN, NaN, 37197362` eight and a half hours
later, while the same window ending 2026-09-02 was clean on every column. Both
look identical from the calendar, so the vendor has to be asked.

When a window is refused, ml4t-data logs it at error level. If you see
`Failed to fetch AAPL: yahoo: Column 'open' contains 1 null values` below and
then a row count on the next line, that is the retreat working: the first
window asked for a bar the vendor has not published, and the second one landed.

The other reason not to call `update()` blind is its `fill_gaps=True` default:
a calendar-unaware gap detector that treats every weekend and US holiday as a
missing trading day and forward-fills it, inflating each symbol's row count by
hundreds of phantom bars. For OHLCV data the right behavior is to leave
non-trading days absent and rely on a calendar-aware completeness check
downstream — which is what section 3 does with `GapDetector`.

```python
print("=== Incremental Update ===")
for symbol in symbols:
    rows = update_through_last_complete_bar(
        dm, storage, symbol, provider=DEMO_PROVIDER, lookback_days=UPDATE_LOOKBACK_DAYS
    )
    print(f"  {symbol}: updated ({rows} rows)")
```

Each symbol started with 502 rows (2023-01 through 2024-12). The update
fetches the 7-day overlap plus every session through the last complete bar and
merges; the resulting row count equals the original 502 plus exactly the new
trading sessions — no synthetic weekend/holiday rows, and no half-formed bar
for the session that is still running.

---

## 2. Update Strategies

ml4t-data supports four strategies for different scenarios:

```python
pl.DataFrame(
    {
        "strategy": ["INCREMENTAL", "APPEND_ONLY", "FULL_REFRESH", "BACKFILL"],
        "behavior": [
            "Fetch data after last stored timestamp",
            "Add new rows; never modify existing",
            "Re-download and replace all data",
            "Fetch missing periods inside existing range",
        ],
        "use_case": [
            "Daily updates (default, fastest)",
            "Audit-safe archives",
            "Recovery after corruption",
            "Patch holes in historical data",
        ],
    }
)
```

The `IncrementalUpdater` class provides low-level control over these strategies.
For most use cases, `DataManager.update()` (which uses `INCREMENTAL`) is sufficient.

---

## 3. Gap Detection

Before running a backtest, verify that your data is complete.
Gaps can occur from failed downloads, provider outages, or holiday handling.

```python
gap_detector = GapDetector(exclude_weekends=True)

print("=== Gap Detection ===")
for symbol in symbols:
    key = f"equities/daily/{symbol}"
    df = storage.read(key).collect()
    gaps = gap_detector.detect_gaps(df, frequency="daily")
    if gaps:
        print(f"{symbol}: {len(gaps)} gap(s) found")
        for gap in gaps[:5]:
            print(f"  {gap['start'].date()} -> {gap['end'].date()} ({gap['size_days']} days)")
        if len(gaps) > 5:
            print(f"  ... and {len(gaps) - 5} more")
    else:
        print(f"{symbol}: complete (no gaps)")
```

`exclude_weekends=True` filters Saturdays and Sundays. US market holidays
(MLK Day, Good Friday, Thanksgiving, etc.) still register as one-day gaps
because the detector has no exchange calendar — fine for routine update
health checks, but pair with a calendar-aware completeness check before
trusting the result for backtest panels.

---

## 4. Data Health Dashboard

In production, monitor data health across your entire universe.

```python
def data_health_report(storage: HiveStorage, symbols: list[str]) -> pl.DataFrame:
    """Per-symbol freshness, gap count, and validation issue count."""
    validator = OHLCVValidator(max_return_threshold=MAX_RETURN_THRESHOLD)
    detector = GapDetector(exclude_weekends=True)
    now = datetime.now()
    rows = []
    for symbol in symbols:
        df = storage.read(f"equities/daily/{symbol}").collect()
        last_date = df["timestamp"].max().replace(tzinfo=None)
        days_stale = (now - last_date).days
        gaps = detector.detect_gaps(df, frequency="daily")
        result = validator.validate(df)
        rows.append(
            {
                "symbol": symbol,
                "status": "stale" if days_stale > STALE_DAYS else "fresh",
                "rows": len(df),
                "last_date": last_date.date(),
                "days_stale": days_stale,
                "gaps": len(gaps),
                "issues": result.error_count if not result.passed else 0,
            }
        )
    return pl.DataFrame(rows)
```

```python
report = data_health_report(storage, symbols)
report
```

```python
fig, axes = plt.subplots(1, 3, figsize=(14, 4), constrained_layout=True)

axes[0].barh(report["symbol"].to_list(), report["rows"].to_list(), color=COLORS["blue"])
axes[0].set_xlabel("Rows")
axes[0].set_title("Rows stored")

stale_days = report["days_stale"].to_list()
freshness_color = [
    COLORS["positive"]
    if d <= FRESH_DAYS
    else COLORS["amber"]
    if d <= STALE_DAYS
    else COLORS["negative"]
    for d in stale_days
]
# A bar of zero width draws nothing, so a fully fresh universe would render a blank panel.
# The markers below are what a reader sees instead of nothing.
axes[1].barh(report["symbol"].to_list(), stale_days, color=freshness_color)
zero_mask = [i for i, d in enumerate(stale_days) if d == 0]
if zero_mask:
    axes[1].scatter(
        [0] * len(zero_mask),
        [report["symbol"].to_list()[i] for i in zero_mask],
        color=[freshness_color[i] for i in zero_mask],
        s=80,
        marker="o",
        zorder=3,
        label="Fresh (0 days)",
    )
axes[1].axvline(
    FRESH_DAYS, color=COLORS["positive"], linestyle="--", alpha=0.6, label=f"{FRESH_DAYS} days"
)
axes[1].axvline(
    STALE_DAYS, color=COLORS["amber"], linestyle="--", alpha=0.6, label=f"{STALE_DAYS} days"
)
axes[1].set_xlim(left=-0.5)
axes[1].set_xlabel("Days Since Update")
axes[1].set_title("Days since last update")
axes[1].legend(fontsize=8, loc="lower right")

gaps = report["gaps"].to_list()
issues = report["issues"].to_list()
axes[2].barh(report["symbol"].to_list(), gaps, color=COLORS["negative"], label="Gaps")
axes[2].barh(
    report["symbol"].to_list(), issues, left=gaps, color=COLORS["amber"], label="Validation"
)
axes[2].set_xlabel("Count")
axes[2].set_title("Gaps and validation flags")
axes[2].legend(fontsize=8, loc="lower right")

fig.suptitle("Per-symbol storage health")
show_with_alt(
    fig,
    "Three horizontal-bar panels sharing a symbol axis. The left shows rows stored per "
    "symbol, with bars of near-equal length. The middle shows days since the last update "
    "against two dashed threshold lines, with a coloured marker at zero for each symbol "
    "that is fully up to date. The right stacks gap counts and validation flags per "
    "symbol.",
)
```

---

## 5. The Book's Update Workflow

The book's `data/download_all.py` script implements exactly this pattern
for all asset classes. Run it with `--update` to extend datasets to the present:

```bash
# Initial download (run once)
python data/download_all.py

# Update to present (run daily/weekly)
python data/download_all.py --update
```

Under the hood, `download_all.py` uses:
- `ETFDataManager.from_config("data/etfs/config.yaml")` → `manager.update()`
- `CryptoDataManager.from_config("data/crypto/config.yaml")` → `manager.update()`
- `MacroDataManager.from_config("data/macro/config.yaml")` → `manager.download_treasury_yields()`
- `FuturesDataManager.from_config("data/futures/config.yaml")` → `manager.download_all()`

Each manager reads its YAML config for symbols, date ranges, and provider settings,
then updates only what's new.

### Config-Driven Downloads

The ETF config at `data/etfs/config.yaml` defines:
```yaml
etfs:
  provider: yahoo
  start: '2006-01-01'
  end: '2025-12-31'
  frequency: daily
  tickers:
    us_equity_broad:
      symbols: [SPY, QQQ, IWM, ...]
    us_sectors:
      symbols: [XLB, XLC, XLE, ...]
    # ... 9 categories, 100 ETFs total
```

When you run `--update` in 2026+, it extends data beyond the configured end date
to the present — no config changes needed.

---

## Summary

### Key Patterns

| Pattern | Command | When |
|---------|---------|------|
| Initial load | `dm.load(symbol, start, end)` | First time |
| Daily update | `dm.update(symbol, lookback_days=7)` | Every trading day |
| Gap check | `updater.detect_gaps(df, "daily")` | Before backtesting |
| Full refresh | `UpdateStrategy.FULL_REFRESH` | After data corruption |
| Batch update | `download_all.py --update` | Cron job |

### Production Checklist

1. **Initial download**: `python data/download_all.py` (run once, ~10 min)
2. **Schedule updates**: Add `download_all.py --update` to cron (daily at 6 PM)
3. **Monitor health**: Check freshness, gaps, and validation before backtests
4. **Validate data**: Use `OHLCVValidator` on every load (see `13_data_quality_framework`)

### Key Takeaways

- **`lookback_days` is the operating lever**, not the symbol set. Set it once
  to cover provider revision windows (Yahoo retro-adjusts ~5 trading days; CRSP
  ~30) and the strategy generalizes across instruments.
- **Gap detection runs against stored data, not stream data.** A daily cron
  that finishes with `detect_gaps()` catches missed trading days before any
  backtest reads stale partitions.
- **Full refresh is the recovery path.** Use `UpdateStrategy.FULL_REFRESH`
  when validation flags a regression; never patch a corrupted parquet in place.
- **Configurable end dates eliminate config drift.** Symbol-list YAMLs use
  today-as-default; `--update` extends data beyond the file's `end:` field
  without needing edits each calendar year.
- **Cron + idempotent CLI is the production interface.** The same
  `download_all.py --update` works in a notebook, a CI run, and a 6 PM cron.

Next: `20_storage_benchmark_file` compares file-format throughput for the
parquet write path implicit in every update; `21_storage_benchmark_database`
extends the same comparison to database engines.

### Cross-References

- **Data quality**: `13_data_quality_framework` — validation and anomaly detection.
- **DataManager basics**: `18_data_management` — fetch, batch, Universe, storage.
- **Storage benchmarks**: `20_storage_benchmark_file` (file formats) and
  `21_storage_benchmark_database` (database engines).
- **Download scripts**: `data/download_all.py` — the book's orchestrator.
- **ml4t-data docs**: [ml4trading.io/docs/data/user-guide/incremental-updates/](https://ml4trading.io/docs/data/user-guide/incremental-updates/)
![notebook output](figures/p1_1.png)