The parquet files are unreadable — and the exchange data window has closed
An algo trader running NSE F&O backtests discovers that the tick-data parquet files for a two-year period are corrupted after an SSD firmware crash. Retail tick data vendors like Zerodha or True Data maintain a limited lookback window — typically 60–90 days. Data older than this window is irreplaceable from commercial sources. If it was on your local SSD and is now corrupted, local data recovery is the only path to restoring historical backtests.
Why tick data parquet files corrupt
Parquet file structure and failure modes
Apache Parquet stores data in row groups with a file footer that contains the schema, column statistics, and row group offsets. Corruption in the file footer makes the entire file unreadable to standard Parquet readers, even if all actual row-group data is intact. A write interrupted during footer flush (power cut, SSD crash) leaves a partial footer. The underlying columnar data is complete.
Recovery for footer-corrupted parquet files
PyArrow’s ParquetFile class can read individual row groups directly, bypassing the corrupt footer. The process: open the file with pq.ParquetFile(path), iterate over row groups with .read_row_group(i), write each to a new file, then concatenate. For large tick datasets (100M+ rows), DuckDB with IGNORE_ERRORS=true can scan and export recoverable rows directly to a new .parquet or .csv output.
SSD failure with tick data inside
If the SSD itself has failed, professional imaging is required before any software recovery. Trading workstation SSDs under high sustained write loads from real-time tick data ingestion can experience firmware crashes or NAND wear — symptoms like slow boot, intermittent non-detection, or clicking on an older HDD in a trading desktop are all indicators of disk failure that need hardware-level imaging before any file recovery. Logical SSD recovery: ₹5,000–₹15,000. Our data recovery service handles all NVMe formats and ships recovered data from any Indian city.
Re-acquiring tick data after recovery fails
True Data provides NSE/BSE tick data from 2010 at ₹8,000–₹25,000 per year. Global Data Feeds offers historical tick data from 2007. For intraday 1-minute OHLCV, Zerodha Kite Connect API provides 60-day history at no extra cost beyond API subscription. These sources do not replace raw tick data but serve as proxy inputs for backtests that do not require sub-minute granularity.
Prevention for Indian algo traders
Implement a three-copy tick data backup: the live SSD (primary), an external NAS or second SSD (local redundancy), and cold cloud storage (AWS S3 Glacier India region — ap-south-1 — at approximately ₹0.30 per GB per month). Script automated daily rsync from the primary SSD to the NAS, and weekly sync to S3 Glacier. A 5-year NSE F&O tick dataset typically runs 200–800 GB — S3 Glacier for 500 GB costs roughly ₹150 per month. See our Data Recovery category for related storage backup strategy guides.
India-specific tick data infrastructure risks
NSE colocation and proximity hosting
Professional Indian algo traders using NSE co-location (near the NSE data center in Mumbai) or proximity hosting services face a different storage risk than retail traders. Co-location servers run Linux with NVMe SSDs configured for sustained high-frequency writes — tick data ingestion at 100K+ events per second during market hours. NAND wear accumulates faster in this environment than on a retail trader’s desktop. Co-location operators typically use enterprise SSDs (Intel D7 series, Samsung PM9A3) with higher endurance ratings, but the fundamentals of SSD failure still apply: firmware crashes, NAND wear-out, and power events are all possible.
Data piping from broker API to local storage
Retail Indian algo traders typically pipe live tick data from Zerodha Kite Connect, Upstox Market Data, or Angel One SmartAPI to local parquet storage using Python scripts. A common failure mode: the Python script writes tick data to a parquet file while appending, and the process crashes mid-write (OOM error, broker API disconnect, power cut). The result is a partial parquet write — the footer is incomplete. As covered above, this is recoverable with PyArrow row-group iteration. But the more robust architecture is to write each day’s tick data to a new parquet file (one file per trading day) rather than a continuously-appended master file. Daily files limit the blast radius of any single crash to one day’s data rather than the entire historical dataset.
Electricity reliability at Indian home offices
The scenario is familiar to anyone running a trading setup from a Mumbai apartment, Bengaluru home office, or Hyderabad flat: a power cut during market hours. A UPS is non-negotiable for a trading workstation. But UPS batteries degrade — a 3-year-old UPS battery may have only 30% of its rated runtime. During a longer power cut, the UPS exhausts its battery and the system shuts down ungracefully. The NVMe SSD receives no warning shutdown signal. Implement a graceful shutdown script that triggers when the UPS reports 20% battery remaining — APC’s PowerChute software or NUT (Network UPS Tools) on Linux both support this. The script should flush all open file handles, stop data ingestion, and trigger a clean OS shutdown before the battery runs out.
The cost of losing backtest history vs preventive storage
A professional Indian algo trader rebuilding 5 years of NSE F&O tick data from True Data or Global Data Feeds pays ₹15,000–₹40,000 for a bulk historical data purchase, plus the time to ingest and validate the data (typically 2–4 weeks of engineering work at ₹2,000–₹5,000 per day for a developer). Total cost: ₹55,000–₹1,20,000. Three-copy backup with NAS and S3 Glacier costs under ₹5,000 per year. The ROI calculation is unambiguous. For traders who have already experienced a data loss event, our data recovery service handles NVMe SSDs from trading workstations with fast turnaround from anywhere in India.
Professional grade storage for Indian trading workstations
A retail Indian algo trader’s storage infrastructure should match the criticality of the data it holds. The minimum viable setup: primary NVMe SSD (Samsung 990 Pro 2 TB at ₹10,000–₹12,000) for the live trading system and database, a secondary SATA SSD (₹4,000) for the nightly rsync backup of the tick data directory, and a ₹150/month S3 Glacier cold storage subscription for the weekly off-site archive. The total storage hardware cost is under ₹15,000 — an amount that should be immediately justifiable given the cost of reconstructing five years of tick data. For traders running a production algo server in a Mumbai or Hyderabad co-location facility: implement ZFS on Linux with RAID-Z2 (equivalent to RAID 6, tolerates two simultaneous drive failures) for the tick data volume. ZFS also provides end-to-end checksumming that detects silent bit rot — a data corruption mode where storage media changes data without the file system detecting it, particularly dangerous for long-term tick data archives. Our workstation service handles NVMe replacement, RAID configuration, and UPS integration for Indian trading workstations in Hyderabad and via courier for other cities.
Emergency recovery during market hours
If your trading workstation SSD fails during active market hours, the priority is halting all open positions before addressing data recovery. Do not continue trading on a system with a failing storage drive — order management system logs, FIX protocol logs, and position data may all be in an inconsistent state. Use a backup trading system (mobile app or second PC) to close or hedge open positions first. Only then address the drive failure. For algo traders who have an active strategy running on a remote server, kill the strategy immediately via the broker’s web interface or API before the algorithm executes further on potentially stale data from a corrupted database. After positions are managed, contact our data recovery service — we handle trading workstation NVMe recoveries on a priority basis and understand that market-hour data loss is a time-critical emergency. Ship the drive to our Secunderabad lab with a same-day courier service from any Indian city.