Crypto Data Lake

A production-ready, local-first cryptocurrency market data pipeline for Windows that collects, transforms, validates, and stores real-time trading data from Binance.

Architecture

Data Flow: Exchange → Collector → Transformer → Parquet Storage → DuckDB → Analysis

Collector: Real-time WebSocket connection to Binance for trade and quote data
Transformer: Converts raw events into 1-second OHLCV bars with L1 quotes
Validator: Ensures data quality, continuity, and schema integrity
Compactor: Merges partitioned files into daily Parquet archives with hash verification

Features

✅ Live streaming data collection (trades + top-of-book quotes)
✅ Historical backfill via Binance REST API (up to 6 months)
✅ Unified orchestrator with auto-transform scheduling
✅ Macro/FX data collection (1-minute from yfinance)
✅ Auto-reconnect with exponential backoff
✅ Rotating JSONL file output (60-second intervals)
✅ 1-second OHLCV aggregation with VWAP and spread
✅ Partitioned Parquet storage (year/month/day)
✅ Data validation (gaps, duplicates, schema checks)
✅ Daily file compaction with SHA256 hashing
✅ Comprehensive logging with 14-day rotation
✅ Full test coverage with pytest

Tech Stack

Python 3.12 (compatible with 3.11+)
aiohttp + websockets: Async WebSocket client
pandas + pyarrow: Data transformation and Parquet I/O
duckdb: Fast analytical queries on Parquet files
loguru: Structured logging
pytest: Unit and integration testing

Quick Start

1. Install Dependencies

python -m venv venv
venv\Scripts\activate  # On Windows
# source venv/bin/activate  # On Linux/Mac
pip install -r requirements.txt

2. Configure

# Copy the example config
cp config.yml.example config.yml

Edit config.yml to set:

base_path: Local data lake directory (default: ./data - relative, works anywhere)
symbols: Trading pairs to track (13 crypto pairs pre-configured)
macro_minute.tickers: Stock/FX/commodity tickers (17 pre-configured)

3. Run Pipeline

# Collect live data (run continuously)
python main.py --mode collector

# Transform raw data to Parquet (run every 5 min or on-demand)
python main.py --mode transformer --date 2025-10-20

# Validate data quality
python main.py --mode validate --date 2025-10-20

# Compact daily files
python main.py --mode compact --date 2025-10-20

Project Structure

crypto-lake/
├── main.py                 # CLI entrypoint
├── config.yml              # Runtime configuration
├── collector/
│   └── collector.py        # WebSocket data collection
├── transformer/
│   └── transformer.py      # Raw → Parquet transformation
├── storage/
│   └── compactor.py        # Daily file compaction
├── tools/
│   ├── common.py           # Shared utilities
│   ├── validator.py        # Data quality checks
│   ├── backfill.py         # Historical data via REST API
│   └── scheduler.py        # Windows Task Scheduler integration
├── sql/
│   └── query_duckdb.sql    # Example analytical queries
└── tests/
    ├── test_collector.py   # Collector unit tests
    └── test_transformer.py # Transformer unit tests

Data Output

Raw Data (JSONL)

D:/CryptoDataLake/raw/binance/SOLUSDT/2025-10-20/part_001.jsonl

Each line: {symbol, ts_event, ts_recv, price, qty, side, bid, ask, stream}

Transformed Data (Parquet)

D:/CryptoDataLake/parquet/binance/SOLUSDT/year=2025/month=10/day=20/*.parquet

Columns: symbol, window_start, open, high, low, close, volume_base, volume_quote, trade_count, vwap, bid, ask, spread

Compacted Data (Daily)

D:/CryptoDataLake/parquet/binance/SOLUSDT/2025-10-20.parquet

Testing

Run the full test suite:

pytest -v

Current Status: ✅ All 4 tests passing

test_parse_event_trade_and_quote: Binance message parsing
test_rotating_writer: JSONL file rotation logic
test_transformer_integration: End-to-end pipeline test
test_aggregate_bars_1s_basic: OHLCV aggregation correctness

Validation Results (Latest)

Date: 2025-10-20 Data Quality:

✅ 321 rows collected (SOLUSDT)
✅ 0 missing seconds
✅ 0 duplicates
✅ All schema columns present
✅ SHA256 hash: 49d27af23d4e64ddea78a63a30ed32064d742a1e9fcc2320fb29c9c4f5816458

Scheduling (Windows)

Set up automated pipeline execution:

python tools/scheduler.py --action setup_all

This creates Windows Task Scheduler jobs:

Collector: Runs on system startup
Transformer: Runs every 5 minutes
Compactor: Runs daily at 1:30 AM

Unified Orchestrator (Recommended)

The orchestrator is a single long-running process that manages both crypto and macro data collection, with built-in health monitoring.

What It Does

The orchestrator runs:

Binance WebSocket Collector - Continuous crypto streaming (19 symbols)
Macro/FX Data Fetcher - Scheduled 1-minute data collection every 15 minutes (SPY, UUP, ES=F, FX pairs)
Transformer - Automatic transformation of raw data to Parquet every 60 minutes
One-Time Startup Backfill - Fetches 7 days of historical macro data on launch
Health Monitoring - Writes metrics every 60 seconds to JSON and Markdown files

How to Run

Using config.yml (recommended):

python main.py --mode orchestrate

With CLI arguments:

python main.py --mode orchestrate \
  --macro_tickers SPY,UUP,ES=F,EURUSD=X,GBPUSD=X,USDJPY=X \
  --macro_interval_min 15 \
  --macro_lookback_startup_days 7 \
  --macro_runtime_lookback_days 1

Configuration

Edit config.yml to set default orchestrator parameters:

transformer:
  resample_interval_sec: 1
  parquet_compression: "snappy"
  schedule_minutes: 60  # Run transformer every 60 minutes (0 to disable)

macro_minute:
  tickers:
    - SPY          # S&P 500 ETF
    - UUP          # US Dollar Index Fund
    - ES=F         # E-mini S&P 500 Futures
    - EURUSD=X     # EUR/USD
    - GBPUSD=X     # GBP/USD
    - USDJPY=X     # USD/JPY
    - USDCAD=X     # USD/CAD
    - USDCHF=X     # USD/CHF
  schedule_minutes: 15            # Fetch macro data every 15 minutes
  startup_backfill_days: 7        # One-time backfill on startup
  runtime_lookback_days: 1        # Fetch last 1 day on each run

Health Monitoring

The orchestrator writes health metrics to two files:

JSON Heartbeat (D:/CryptoDataLake/logs/health/heartbeat.json):

{
  "ts_utc": "2025-10-22T12:00:00Z",
  "collector": {
    "status": "running",
    "last_latency_p50_ms": 10.5,
    "last_latency_p95_ms": 25.3,
    "last_seen_ts": "2025-10-22T11:59:00Z"
  },
  "macro_minute": {
    "status": "idle",
    "last_run_start": "2025-10-22T11:45:00Z",
    "last_run_end": "2025-10-22T11:46:00Z",
    "last_run_rows_written": 1500,
    "last_error": null
  },
  "transformer": {
    "status": "idle",
    "last_run_start": "2025-10-22T12:00:00Z",
    "last_run_end": "2025-10-22T12:02:00Z",
    "last_error": null
  },
  "files": {
    "raw_count_today": 100,
    "parquet_1s_rows_today": 50000,
    "macro_min_rows_today": 2000
  }
}

Markdown Report (D:/CryptoDataLake/reports/health.md):

Overall system status (HEALTHY / ERROR / STOPPED)
Real-time crypto collector metrics
Macro/FX fetcher metrics
Today's data volume statistics

How to Stop

Press Ctrl+C to gracefully stop the orchestrator. All threads will shut down cleanly within 10 seconds, and a final heartbeat will be written.

Data Collection Schedule

Crypto Data: Continuous streaming, files rotate every 60 seconds
Macro Data:
- Startup: 7-day backfill (happens once when orchestrator starts)
- Runtime: Fetches every 15 minutes (or as configured)
- Each run: Fetches last 1 day with automatic deduplication

Notes

Macro data is fetched via yfinance REST API (1-minute granularity, max 7 days)
Crypto data is collected via Binance WebSocket (real-time streaming)
Both data types are stored in partitioned Parquet format (year/month/day)
Health metrics update every 60 seconds (configurable in code)
All timestamps are UTC timezone-aware

Testing Mode

Testing mode allows you to validate the entire Crypto Lake system in minutes instead of hours by compressing time-based intervals while maintaining all production logic, data integrity checks, and warnings.

When to Use Testing Mode

🔍 Validate end-to-end pipeline after code changes
🚀 Quick smoke test before production deployment
🧪 Development and debugging with accelerated cycles
📊 Verify all components work together correctly

How Testing Mode Works

Testing mode applies time compression to scheduled operations:

Operation	Production	Testing	Speedup
Transform Interval	60 min	2 min	30x faster
Macro Fetch Interval	15 min	1 min	15x faster
Macro Startup Backfill	7 days	1 day	7x less data
Backfill Lookback	180 days	3 days	60x less data

What Stays the Same:

✅ All business logic and data processing
✅ All validation rules and integrity checks
✅ All warnings and error handling
✅ Full 19-symbol collection (no shortcuts)
✅ Real-time WebSocket streaming
✅ Parquet schema and partitioning

What Changes:

📁 Output directory: D:/CryptoDataLake/test/ (isolated)
📝 Log files: D:/CryptoDataLake/test/logs/test/ (isolated)
⏱️ Intervals compressed for speed
🏷️ All health reports labeled "MODE: TEST"
⚡ Forced initial transform after 2-minute warmup

Running in Testing Mode

Method 1: Shorthand flag

python main.py --mode test

Method 2: Orchestrate with testing flag

python main.py --mode orchestrate --testing

Method 3: Enable in config.yml

testing:
  enabled: true  # Activates testing mode globally

Test Workflow Example

A typical 5-minute test run validates:

Startup (0:00-0:30): Orchestrator initializes, starts WebSocket collector
Macro Backfill (0:30-1:00): One-time 1-day macro data fetch
Forced Transform (2:00): Guaranteed transform cycle runs
Scheduled Transform (4:00): Second transform cycle completes
Macro Fetch (3:00): Scheduled macro data fetch
Shutdown (5:00): User presses Ctrl+C, test summary prints

Test Summary Output

When you stop the orchestrator with Ctrl+C, you'll see:

================================================================================
TEST COMPLETE
================================================================================
Duration: 300.5s
Transform cycles run: 2
Macro fetches: 2
Files written: 16
Warnings: None
================================================================================

Configuration

All testing parameters are defined in config.yml:

testing:
  enabled: false                  # Can be overridden by CLI flags
  transform_interval_min: 2       # Transform every 2 minutes
  macro_interval_min: 1           # Fetch macro data every 1 minute
  macro_lookback_startup_days: 1  # Backfill 1 day on startup
  macro_runtime_lookback_days: 1  # Fetch 1 day on each run
  backfill_days: 3                # Backfill 3 days for testing
  base_path: "D:/CryptoDataLake/test"  # Isolated test directory

Validating Test Results

After running in test mode, validate the output:

# Run final validation on test directory
python -m tools.final_validation

# Check health report
cat D:/CryptoDataLake/test/reports/health.md

# Inspect test logs
cat D:/CryptoDataLake/test/logs/test/orchestrator.log

The validation script automatically detects test mode from config.yml and validates the test directory.

Notes

Isolation: Test mode uses completely separate directories - production data is never touched
No Shortcuts: All 19 symbols are collected, all validations run, all warnings fire
Realistic Testing: Only time intervals are compressed - everything else is production-grade
Clean State: Delete D:/CryptoDataLake/test/ between test runs for clean testing
Performance: Expect ~50-100 MB of test data after a 5-minute run

Test GUI (Streamlit)

Launch the interactive dashboard for data visualization and quality monitoring:

streamlit run gui/app.py

Features:

Real-time Candlestick Charts: View OHLC data across multiple timeframes (1s, 1m, 5m, 15m, 1h)
Volume Analysis: Secondary volume bars color-coded by price direction
Spread Visualization: Track bid-ask spread over time
Data Quality Metrics: Monitor gaps, completeness, and continuity
Auto-Refresh: Enable overnight monitoring with configurable intervals (15s - 5m)
Export: Download filtered data as CSV

Access: http://localhost:8501 after launch

Tips:

Use auto-refresh with 60s interval for overnight monitoring
All timestamps displayed in UTC
Efficiently queries large datasets using DuckDB with time-based filtering

Analysis-Ready Layer

The crypto-lake includes an analysis-ready layer with research views, data slicing tools, and comprehensive validation:

Research Views (DuckDB)

Pre-built views make analysis "one SELECT away". All views use window_start as the timestamp field (UTC timezone-aware).

Available Views:

bars_1s - Base 1-second bars from collector (with exchange literal)
bars_1m - 1-minute rollup from 1s bars
klines_1m - Official Binance 1m klines (if available)
compare_our_vs_kline_1m - Comparison view for data quality checks
funding_oi_hourly - Funding rates and open interest (if derivatives data exists)
macro_minute - 1-minute macro data (SPY, UUP, ES=F, etc.)
dxy_synthetic_minute - DXY synthetic index (if available)

Location: sql/views.sql (uses @@BASE@@ placeholder for base path)

Note: The views use a @@BASE@@ placeholder that is replaced at runtime with the actual base_path from config.yml. This allows the SQL views to be portable across different installations.

Data Slice Tool

Export timeboxed datasets for analysis in Parquet or CSV format:

# Export 1-minute bars for specific symbols and time range
python main.py --mode slice \
    --symbols SOLUSDT,SUIUSDT \
    --start 2025-10-21T00:00:00Z \
    --end 2025-10-21T23:59:00Z \
    --tf 1m \
    --source bars \
    --out data/extracts/sol_1m_2025-10-21.parquet \
    --format parquet

# Export to CSV instead
python main.py --mode slice \
    --symbols SOLUSDT \
    --start 2025-10-21T00:00:00Z \
    --end 2025-10-21T01:00:00Z \
    --tf 1s \
    --source bars \
    --out data/extracts/sol_1s_sample.csv \
    --format csv

Parameters:

--symbols: Comma-separated list of symbols (e.g., SOLUSDT,SUIUSDT)
--start / --end: Time range in ISO format with Z suffix
--tf: Timeframe (1s or 1m)
--source: Data source (bars or klines)
--out: Output file path
--format: Output format (parquet or csv)

Data Quality Validation

Run comprehensive data quality checks and generate Markdown reports:

# Run validation rulepack
python main.py --mode validate_rules \
    --symbols SOLUSDT,SUIUSDT,ADAUSDT \
    --start 2025-10-21T00:00:00Z \
    --end 2025-10-21T14:40:00Z \
    --tf 1s \
    --source bars \
    --report reports/sanity_2025-10-21.md

Validation Rules:

R1: OHLC ordering (low ≤ open,close ≤ high)
R2: Non-negative prices (OHLC > 0)
R3: Ask ≥ Bid (bars only, ignores NaN)
R4: No NaNs in OHLC (allows NaN in bid/ask/spread)
R5: Timestamp continuity and UTC minute alignment
R6: Spread sanity (spread ≥ 0, < 5% mid for >99.9% of rows)
R7: Parity check with official klines (optional, if available)

Report Output:

Summary table with violation counts per rule
Top 25 offending rows per rule
Overall assessment: PASS / INVESTIGATE / FAIL
Detailed statistics (spread outliers, volume differences, etc.)

Example Report Structure:

# Data Quality Validation Report

## Run Metadata
- Symbols: SOLUSDT, SUIUSDT, ADAUSDT
- Time Range: 2025-10-21T00:00:00Z to 2025-10-21T14:40:00Z
- Timeframe: 1s
- Total Rows Checked: 157,680

## Validation Summary
| Rule | Description | Total Checked | Violations | % Violated |
|------|-------------|---------------|------------|------------|
| R1   | OHLC ordering | 157,680 | 0 | 0.00% |
| R2   | Positive prices | 157,680 | 0 | 0.00% |
...

## Overall Assessment
✅ PASS - All validation rules passed successfully.

Binance Historical Backfill

Fetch historical 1-minute OHLCV data from Binance REST API to backfill up to 6 months of data:

# Backfill 180 days of historical data
python main.py --mode backfill_binance \
    --symbols SOLUSDT,BTCUSDT,ETHUSDT \
    --lookback_days 180 \
    --interval 1m

Features

REST API Integration: Uses Binance /api/v3/klines endpoint
Daily Chunking: Fetches data in daily chunks to respect API limits
Automatic Deduplication: Checks existing data and only writes new timestamps
Rate Limit Handling: Exponential backoff with retry logic (up to 5 attempts)
Partitioned Storage: Writes to D:/CryptoDataLake/backfill/binance/{SYMBOL}/year=YYYY/month=MM/day=DD/
UTC Timestamps: All timestamps normalized to UTC timezone

Schema

open_time: Candle open timestamp (UTC)
open, high, low, close: Price data
volume: Base asset volume
close_time: Candle close timestamp (UTC)
quote_volume: Quote asset volume
trades: Number of trades in candle
taker_base_vol, taker_quote_vol: Taker volume metrics

Use Cases

Historical Analysis: Backfill 6 months for backtesting strategies
Gap Filling: Fill gaps from collector downtime
Bootstrap: Initialize database before starting real-time collection

Notes

Binance limits: 1000 klines per request, 1200 requests/minute
Rate limiting: 0.1s sleep between requests (10 req/s max)
Deduplication: Existing timestamps are automatically skipped
Storage: Separate from real-time data (backfill/ vs parquet/)

Macro Data Collection

Collect 1-minute macro data (stocks, ETFs, futures) using yfinance:

# Fetch SPY and UUP data for last 7 days
python main.py --mode macro_minute \
    --tickers SPY,UUP,ES=F \
    --lookback_days 7

Storage: D:/CryptoDataLake/macro/minute/{ticker}/year=2025/month=10/day=21/

Schema: ticker, ts, open, high, low, close, volume (all UTC timezone-aware)

DuckDB Queries

Load and analyze data:

-- Load 1-second bars
SELECT * FROM read_parquet('D:/CryptoDataLake/parquet/**/*.parquet');

-- Aggregate to 1-minute bars
SELECT
    symbol,
    date_trunc('minute', window_start) AS ts,
    first(open) AS open,
    max(high) AS high,
    min(low) AS low,
    last(close) AS close,
    sum(volume_base) AS volume
FROM read_parquet('D:/CryptoDataLake/parquet/**/*.parquet')
GROUP BY symbol, ts
ORDER BY ts;

Build Verification

The codebase has been fully tested and verified:

✅ Environment: Python 3.12.6, all dependencies installed
✅ Static Analysis: All modules import successfully, no syntax errors
✅ Unit Tests: 4/4 passing
✅ Functional Tests: All 4 pipeline modes verified with live data
✅ Data Quality: Zero gaps, zero duplicates, full schema compliance

See docs/ for the complete build verification report.

Notes

High latency warnings during collection are normal (caused by Binance event timestamp metadata)
The collector auto-reconnects on network failures with exponential backoff
Logs rotate daily and are retained for 14 days
Parquet files use Snappy compression for optimal size/speed tradeoff

License

MIT

Name		Name	Last commit message	Last commit date
Latest commit History 49 Commits
.claude		.claude
.github/workflows		.github/workflows
api		api
collector		collector
deploy		deploy
docs		docs
gui		gui
qa		qa
reports		reports
sql		sql
storage		storage
tests		tests
tools		tools
transformer		transformer
.gitignore		.gitignore
CLAUDE.md		CLAUDE.md
CLOUD_FUNCTIONAL_PARITY_AUDIT.md		CLOUD_FUNCTIONAL_PARITY_AUDIT.md
CLOUD_READINESS_AUDIT.md		CLOUD_READINESS_AUDIT.md
Dockerfile		Dockerfile
GCP_OPERATIONS_GUIDE.txt		GCP_OPERATIONS_GUIDE.txt
LOCAL_FILES_MIGRATION_AUDIT.md		LOCAL_FILES_MIGRATION_AUDIT.md
MIGRATION.md		MIGRATION.md
PHASE1_DEPLOYMENT_GUIDE.md		PHASE1_DEPLOYMENT_GUIDE.md
README.md		README.md
STORAGE_TROUBLESHOOTING.md		STORAGE_TROUBLESHOOTING.md
config.yml		config.yml
config.yml.example		config.yml.example
main.py		main.py
requirements-qa.txt		requirements-qa.txt
requirements-sql.txt		requirements-sql.txt
requirements.txt		requirements.txt

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Crypto Data Lake

Architecture

Features

Tech Stack

Quick Start

1. Install Dependencies

2. Configure

3. Run Pipeline

Project Structure

Data Output

Raw Data (JSONL)

Transformed Data (Parquet)

Compacted Data (Daily)

Testing

Validation Results (Latest)

Scheduling (Windows)

Unified Orchestrator (Recommended)

What It Does

How to Run

Configuration

Health Monitoring

How to Stop

Data Collection Schedule

Notes

Testing Mode

When to Use Testing Mode

How Testing Mode Works

Running in Testing Mode

Test Workflow Example

Test Summary Output

Configuration

Validating Test Results

Notes

Test GUI (Streamlit)

Analysis-Ready Layer

Research Views (DuckDB)

Data Slice Tool

Data Quality Validation

Binance Historical Backfill

Features

Schema

Use Cases

Notes

Macro Data Collection

DuckDB Queries

Build Verification

Notes

License

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