mif-dqf 1.2.0

Production-ready validation framework for OHLCV financial data

DQF - Data Quality Framework

MIF-certified data quality framework for OHLCV financial data.

DQF v1.1 validates financial time series through CORE and ADVISORY checks, produces MIF-Lite manifests with a cryptographic MIF-UID and a MIF Purity Index (MPI), and supports two operational modes: CERTIFICATION (strict, deterministic) and DIAGNOSTIC (advisory, flexible).

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🎯 Why DQF Exists

The Fundamental Problem

Garbage In, Garbage Out (GIGO): No matter how sophisticated your trading algorithms or statistical models, if your input data is corrupted, all results are invalid.

Statistical Reality:

• 80% of quantitative strategies fail in production not because of flawed logic, but because of corrupted data during development
• Data quality issues are detected on average 6 months after deployment
• A single corrupted data point can invalidate months of backtesting

The Philosophy of Purification

DQF embodies the principle of systematic purification before critical operations:

Historical Precedents:

• Medicine: Hand washing before surgery (Semmelweis, 1847) - reduced mortality from 18% to 2%
• Laboratory Science: Sterile technique before experiments - ensures reproducibility
• Software Engineering: Input validation before processing - prevents crashes
• Quantitative Finance: DQF (data purification) before analysis - guarantees validity

Cultural Parallels (methodological, not spiritual):

• Islam: Wuḍū (ablution) - 7 ritual cleansings before Salat (prayer)
• Shinto: Temizuya (water purification) before entering a shrine
• Laboratory: Autoclave sterilization before cell culture
• DQF: 7 systematic checks before quantitative analysis

Core Principle: Without purification, the critical operation (analysis/trading) produces unreliable results.

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✨ What DQF Does

Dual Mission

1. Validation: Detect and report data quality issues

• Identifies violations of market physics (H<L, negative volume, etc.)
• Detects statistical anomalies (extreme returns, forward-fill abuse)
• Validates structural integrity (timezone, calendar, duplicates)

2. Purification: Generate certified clean datasets

• Produces validated DataFrames with full provenance tracking
• Guarantees reproducibility (same data → same results, always)
• Enables consistent analysis across teams and time

The DQF Guarantee

When DQF reports status: PASS:

• ✅ Data respects market physics laws
• ✅ No statistical anomalies detected
• ✅ Complete provenance chain tracked
• ✅ Dataset certified for production use

This is not just validation - it's data certification.

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🔬 Core Benefits

For Quantitative Researchers

Problem: Corrupted data during backtesting → false conclusions

# Without DQF: Unknown data quality
backtest_results = strategy.run(data)  # 💥 May be invalid
paper.publish(backtest_results)        # 💥 Non-reproducible

Solution: Certified clean data → reliable backtests

# With DQF: Certified data quality
report = validator.validate(data)
if report.overall_status == "PASS":
    backtest_results = strategy.run(report.cleaned_data)  # ✅ Valid
    paper.publish(backtest_results)                       # ✅ Reproducible

Benefits:

• ✅ Reproducible research (same data → same results)
• ✅ Peer review confidence (provenance tracking)
• ✅ Publication credibility (certified datasets)

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For Trading Systems

Problem: Data corruption in production → catastrophic losses

# Without DQF: Unknown data quality
live_data = fetch_latest()
signal = model.predict(live_data)  # 💥 May be based on corrupted data
execute_trade(signal)              # 💥 Potential disaster

Solution: Real-time validation → safe trading

# With DQF: Real-time validation
live_data = fetch_latest()
report = validator.validate(live_data)

if report.overall_status == "PASS":
    signal = model.predict(report.cleaned_data)  # ✅ Safe
    execute_trade(signal)                        # ✅ Confident
else:
    alert_team(report.all_issues)  # 🚨 Data quality issue
    halt_trading()                 # Safety first

Benefits:

• ✅ Risk mitigation (detect issues before trading)
• ✅ Regulatory compliance (audit trail)
• ✅ Post-mortem analysis (provenance tracking)

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For Data Engineers

Problem: Silent data corruption in pipelines

# Without DQF: Silent failures
raw_data = extract_from_source()
transformed = apply_transformations(raw_data)  # 💥 May propagate corruption
load_to_warehouse(transformed)                 # 💥 Garbage persisted

Solution: Validation checkpoints → data integrity

# With DQF: Validated pipeline
raw_data = extract_from_source()

# Checkpoint 1: Validate raw data
raw_report = validator.validate(raw_data)
assert raw_report.overall_status == "PASS"

transformed = apply_transformations(raw_report.cleaned_data)

# Checkpoint 2: Validate transformed data
final_report = validator.validate(transformed)
assert final_report.overall_status == "PASS"

load_to_warehouse(final_report.cleaned_data)  # ✅ Only clean data persisted

Benefits:

• ✅ Early detection (issues caught immediately)
• ✅ Data lineage (full provenance chain)
• ✅ Quality metrics (SLA monitoring)

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🚀 Quick Start

Installation

pip install mif-dqf

Note — package name vs import name: the PyPI package is mif-dqf but the Python import is from dqf import ... (not import mif_dqf). This is intentional: dqf is the canonical module namespace.

Basic Usage

import pandas as pd
from dqf import DQFValidator, DQFConfig, DQFMode

# Load your data (timezone-aware index required)
data = pd.read_csv("spy.csv", index_col=0, parse_dates=True)
data.index = data.index.tz_localize("UTC")

# CERTIFICATION mode — strict, deterministic, calendar required
config = DQFConfig(mode=DQFMode.CERTIFICATION)
validator = DQFValidator(config)
report = validator.validate(data, calendar="NYSE")

if report.is_certified:
    print(f"✅ CERTIFIED  MPI={report.purity_index:.1f}/100  gate={report.precondition_gate}")
    print(f"   UID: {report.mif_uid}")
    clean_data = report.cleaned_data   # validated DataFrame
    report.print_summary()             # human-readable summary
else:
    print(f"❌ {report.overall_status}  gate={report.precondition_gate}")
    print(f"   CORE:     {report.core_results}")
    print(f"   ADVISORY: {report.advisory_results}")

DIAGNOSTIC mode (no calendar required, useful for exploration):

config = DQFConfig(mode=DQFMode.DIAGNOSTIC)
report = DQFValidator(config).validate(data)
print(f"Status: {report.overall_status}  MPI: {report.purity_index:.1f}")

Output (CERTIFICATION, clean data):

✅ CERTIFIED  MPI=100.0/100  gate=1.0
   UID: sha256:a3f9...

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📋 DQF v1.1 Checks

CORE checks — failure → STATUS_VOID, precondition_gate = 0.0

ID	Check	Purpose
PROD	Envelope seal	Output trust mechanism — always injected PASS
C2	OHLCV Integrity	Market physics (H≥L, H≥O/C, V≥0, no NaN)
C3	Calendar Alignment	Declared calendar required in CERTIFICATION mode
C5	Index Traceability	Unique, chronological, timezone-aware index

ADVISORY checks — warn → STATUS_WARNING, gate capped by MPI

ID	Check	Purpose
C1	Source Uniqueness	Single canonical source (SKIP in Phase 1 — DAL pending)
C4	Forward-Fill Detection	Detects interpolation abuse (consecutive repeats)

Removed in v1.1: C6 (Sanity Tests) migrated to MIF Layer 1; C7 (Logging) replaced by PROD envelope.

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🎓 Complete Examples

Example 1: Research Workflow

import pandas as pd
from pathlib import Path
from dqf import DQFValidator, DQFConfig, DQFMode

# Research scenario: Certifying historical data for a paper
data = pd.read_csv("spy_2020_2024.csv", index_col=0, parse_dates=True)
data.index = data.index.tz_localize("UTC")

config = DQFConfig(mode=DQFMode.CERTIFICATION)
report = DQFValidator(config).validate(data, calendar="NYSE")

if report.is_certified:
    # Save certified dataset with provenance
    report.cleaned_data.to_csv("spy_2020_2024_certified.csv")
    Path("provenance_spy.json").write_text(report.to_json())

    print(f"✅ CERTIFIED  MPI={report.purity_index:.1f}/100")
    print(f"   MIF-UID: {report.mif_uid}")
else:
    print(f"❌ {report.overall_status} (gate={report.precondition_gate})")
    print(f"   CORE failures: {report.core_results}")

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Example 2: Production Pipeline

import logging
from dqf import DQFValidator, DQFConfig, DQFMode

logger = logging.getLogger(__name__)

# Shared validator — reuse across calls (thread-safe for validate())
_config    = DQFConfig(mode=DQFMode.CERTIFICATION, c4_warn_threshold=1)
_validator = DQFValidator(_config)

def validate_daily_data(symbol: str, calendar: str, data: pd.DataFrame) -> pd.DataFrame:
    """Certify daily data; raise on VOID."""
    report = _validator.validate(data, calendar=calendar)

    if report.overall_status == "VOID":
        logger.critical("%s: VOID  core=%s", symbol, report.core_results)
        raise ValueError(f"CORE failure for {symbol} — gate=0")

    if report.overall_status == "WARNING":
        logger.warning("%s: WARNING  advisory=%s  MPI=%.1f",
                       symbol, report.advisory_results, report.purity_index)

    logger.info("%s: %s  MPI=%.1f  UID=%s",
                symbol, report.overall_status, report.purity_index, report.mif_uid)
    return report.cleaned_data

# Usage
try:
    clean = validate_daily_data("SPY", "NYSE", raw_data)
    load_to_warehouse(clean)
except ValueError as exc:
    alert_team(str(exc))
    halt_pipeline()

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Example 3: Batch Processing

from pathlib import Path
from dqf import DQFValidator, DQFConfig, DQFMode

CALENDAR = {"BTC-USD": "CRYPTO_247", "ETH-USD": "CRYPTO_247",
            "SPY": "NYSE", "GLD": "NYSE"}

config    = DQFConfig(mode=DQFMode.CERTIFICATION)
validator = DQFValidator(config)
results   = {}

for symbol, calendar in CALENDAR.items():
    data = pd.read_csv(f"{symbol}.csv", index_col=0, parse_dates=True)
    data.index = data.index.tz_localize("UTC")
    results[symbol] = validator.validate(data, calendar=calendar)
    print(f"{symbol}: {results[symbol].overall_status}  MPI={results[symbol].purity_index:.1f}")

# Keep only certified datasets
certified = {s: r.cleaned_data for s, r in results.items() if r.is_certified}
print(f"\n{len(certified)}/{len(CALENDAR)} datasets CERTIFIED")

# Persist manifests
for symbol, report in results.items():
    Path(f"manifests/{symbol}.mif.json").write_text(report.to_json())

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Example 4: Custom Check

See examples/04_custom_check.py for a complete example. Custom checks extend BaseCheck and are added to DQFValidator._checks before calling validate().

from dqf import DQFValidator, DQFConfig, DQFMode
from dqf.checks.base import BaseCheck
from dqf.core.enums import STATUS_FAIL, STATUS_PASS

class LiquidityCheck(BaseCheck):
    """Advisory check: minimum daily volume."""

    def __init__(self, min_vol: float = 1_000_000):
        super().__init__(check_id="check_custom_liquidity",
                         check_name="Minimum Liquidity")
        self.min_vol = min_vol

    def run(self, data, **kwargs):
        low = (data["volume"] < self.min_vol).sum()
        if low:
            return self._create_result(
                status=STATUS_FAIL,
                message=f"{low} days below minimum volume ({self.min_vol:,.0f})",
                details={"low_volume_days": low},
            )
        return self._create_result(status=STATUS_PASS, message="Liquidity OK")

config    = DQFConfig(mode=DQFMode.DIAGNOSTIC)
validator = DQFValidator(config)
validator._checks["C_LIQ"] = LiquidityCheck(min_vol=500_000)
report    = validator.validate(data)

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🏗️ Architecture

┌─────────────────────────────────────────┐
│   Input: Raw DataFrame (OHLCV)         │
│   - Potentially corrupted               │
│   - Unknown quality                     │
└────────────────┬────────────────────────┘
                 ↓
┌─────────────────────────────────────────┐
│   DQFValidator (mode: CERT | DIAG)     │
│  CORE checks (failure → VOID)          │
│  ┌─────────────────────────────────┐   │
│  │ C2. OHLCV Integrity             │   │
│  │ C3. Calendar Alignment          │   │
│  │ C5. Index Traceability          │   │
│  └─────────────────────────────────┘   │
│  ADVISORY checks (warn → WARNING)      │
│  ┌─────────────────────────────────┐   │
│  │ C1. Source Uniqueness (SKIP/P1) │   │
│  │ C4. Forward-Fill Detection      │   │
│  └─────────────────────────────────┘   │
└────────────────┬────────────────────────┘
                 ↓
┌─────────────────────────────────────────┐
│   PROD Envelope (MIF-Lite manifest)    │
│  ┌─────────────────────────────────┐   │
│  │ MIF-UID  = SHA-256(hash+ver+cal)│   │
│  │ MPI      = 100×(1−Σwᵢ/N)       │   │
│  │ gate     = 1.0/0.8/0.0         │   │
│  └─────────────────────────────────┘   │
└────────────────┬────────────────────────┘
                 ↓
┌─────────────────────────────────────────┐
│   Output: DQFReport (.mif.json)        │
│  - overall_status: CERTIFIED/WARNING/  │
│                    VOID                │
│  - purity_index: 0–100 (MPI)          │
│  - precondition_gate: 0.0/0.8/1.0     │
│  - mif_uid: sha256:...                 │
│  - cleaned_data: validated DataFrame   │
└─────────────────────────────────────────┘

Design Principles:

• Deterministic: Same data + same args → Same MIF-UID (always)
• Dual mode: CERTIFICATION (strict) vs DIAGNOSTIC (advisory)
• MPI: continuous purity score replaces binary PASS/FAIL
• Production-Ready: 189/189 tests passing

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📖 Documentation

• DQF Specification: Canonical architectural decisions (v1.1 design)
• API Reference: Complete API documentation
• Architecture: Design patterns and technical decisions
• Troubleshooting: Common issues and solutions
• Examples: 4 complete examples (basic, config, batch, custom)

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🧪 Testing & Quality

# Run all tests
pytest tests/ -v                    # 189/189 passing

# Coverage
pytest tests/ --cov=dqf

# Examples
python examples/01_basic_validation.py    # ✅ Works
python examples/02_custom_config.py       # ✅ Works
python examples/03_batch_processing.py    # ✅ Works
python examples/04_custom_check.py        # ✅ Works

Quality Metrics:

• 189 tests (164 unit + 25 integration)
• 0 failures

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📦 Project Structure

dqf/
├── dqf/                          # Source code
│   ├── checks/                  # C1–C5 checks (v1.1.0)
│   ├── core/                    # Config, Validator, Report, PRODEnvelope
│   └── utils/                   # Calendar utilities, MPI
├── tests/                       # Test suite (189 tests)
│   ├── unit/                    # Per-module unit tests
│   └── integration/             # End-to-end pipeline tests
├── examples/                    # Complete examples (4)
├── docs/
│   ├── DQF_SPECIFICATION.md     # Canonical specification (v1.1)
│   ├── API.md                   # API reference
│   ├── ARCHITECTURE.md          # Design & patterns
│   └── TROUBLESHOOTING.md       # Common issues
├── scripts/
│   └── test_install.py          # Installation smoke test
├── pyproject.toml               # Package metadata
└── LICENSE                      # MIT License

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🛠️ Development

Requirements

• Python 3.10+
• pandas >= 2.0.0
• PyYAML >= 6.0

Setup

# Clone repository
git clone https://github.com/symbioticode/mif-dqf.git
cd mif-dqf

# Install in editable mode
pip install -e .

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/ -v

Contributing

Contributions welcome! Please see CONTRIBUTING.md for guidelines.

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📊 Benchmarks

Performance (100 days of data):

Total validation time: ~0.6s
  - C2 (Integrity):    0.32s
  - C3 (Calendar):     0.10s
  - C4 (Ffill):        0.10s
  - C5 (Index):        0.08s
  - PROD Envelope:     <0.01s

Scalability:

• 100 days: ~0.6s
• 1,000 days: ~2.0s
• 10,000 days: ~15s

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🗺️ Roadmap

v1.0.0 (legacy)

• 7 checks (Source, Integrity, Calendar, Forward-Fill, Index, Sanity, Logging)
• Binary PASS/FAIL report

v1.1.0 (current) ✅

• ✅ Two operational modes: CERTIFICATION (strict) vs DIAGNOSTIC (advisory)
• ✅ CORE/ADVISORY check classification — CORE failure → VOID, gate=0
• ✅ PROD envelope produces MIF-Lite manifest (.mif.json)
• ✅ MIF Purity Index (MPI) — 0–100 continuous purity score
• ✅ MIF-UID — SHA-256(data_hash + dqf_version + calendar + mode)
• ✅ C6 (Sanity) migrated to MIF Layer 1; C7 (Logging) replaced by PROD envelope
• ✅ 189/189 tests passing

v1.2.0 — Active cleaning (planned)

• Optional deterministic data transformation in CERTIFICATION mode
• Before/after diff reports

v2.0.0 — MIF integration (planned)

• DAL integration (get_certified_data())
• C1 (Source Uniqueness) activated — DAL handoff
• Full provenance chain: source → DQF → MIF

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🤝 Ecosystem

DQF is the foundational layer of the MIF (Metric Integrity Framework) ecosystem.

MIF Layers 1–5  = Metric certification & strategy validation
       ↑           (score capped if DQF precondition fails)
     DAL         = Multi-source data abstraction [planned]
       ↑
     DQF         = Data quality gate [YOU ARE HERE]
       ↑
Raw Sources     = Yahoo Finance, Binance, Kraken, etc.

DQF acts as a precondition_gate: if data does not pass DQF, downstream MIF scores are bounded regardless of metric quality. See DQF_SPECIFICATION.md for the full integration contract.

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📄 License

MIT License - see LICENSE file for details.

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🙏 Acknowledgments

• Methodology: Systematic purification as scientific hygiene
• Inspiration: Medical sterilization, laboratory protocols
• Cultural parallels: Islamic Wudu, Shinto Temizuya (ritual, not spiritual)
• Tools: pandas, pytest, PyYAML

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📞 Contact & Support

• Repository: github.com/symbioticode/mif-dqf
• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [e-mail]

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⭐ Star History

If DQF helps your research or trading, please consider giving it a star! ⭐

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Made with rigor by the DQF Team

"Data hygiene is not optional. It's the foundation of reliable quantitative analysis."