# Create MASTER file (simplified) master_path = os.path.join(output_folder, 'MASTER') with open(master_path, 'wb') as f: # Write minimal master record for one security # Structure is complex; for real use, copy from existing MASTER # This is a simplified placeholder f.write(security_name.encode('ascii') + b'\x00' * (32 - len(security_name))) f.write(struct.pack('<H', 1)) # 1 = stock type f.write(struct.pack('<H', 0)) # data format
| Field | Bytes | Type | Example | |--------|-------|------|---------| | Date | 4 | Signed long int | 20241231 (YYYYMMDD) | | Open | 4 | Float | 150.25 | | High | 4 | Float | 152.00 | | Low | 4 | Float | 149.50 | | Close | 4 | Float | 151.75 | | Volume | 4 | Signed long int | 1234567 | | Open Interest | 4 | Float | 0 |
Once done, your CSV data will function exactly like native MetaStock data, allowing full charting, backtesting, and scanning. convert csv to metastock format
Then update the MASTER file with all security names (requires binary editing or use a tool like ). Best Free Tools Summary | Tool | Platform | Ease of Use | |------|----------|-------------| | MetaStock Converter (MSconv) | Windows | Easy | | Python script (above) | Any | Moderate | | Excel + Binary editor | Windows | Hard | | Notepad++ + Hex plugin | Windows | Very Hard | Final Checklist ✅ CSV has headers: Date, Open, High, Low, Close, Volume ✅ Dates converted to YYYYMMDD integers ✅ Data sorted newest to oldest (descending) ✅ Volume is integer, prices are floats ✅ Output folder path contains no spaces or special characters ✅ MetaStock is closed during file write (to avoid locking)
# Create output folder if not exists os.makedirs(output_folder, exist_ok=True) # Create MASTER file (simplified) master_path = os
File size in bytes ÷ 28 = Number of records Example: 2800 bytes ÷ 28 = 100 days of data. Using Python, loop through a folder:
# Write to MetaStock .DAT file dat_path = os.path.join(output_folder, 'F00001.DAT') with open(dat_path, 'wb') as f: for record in data: # Pack: date (long), open (float), high (float), low (float), # close (float), volume (long), open interest (float) packed = struct.pack( '<lffffl f', # < = little-endian, l = long, f = float record['date'], record['open'], record['high'], record['low'], record['close'], record['volume'], record['open_interest'] ) f.write(packed) Using Python, loop through a folder: # Write to MetaStock
import struct import os import csv from datetime import datetime def csv_to_metastock(csv_path, output_folder, security_name): """ Convert CSV file to MetaStock format. CSV must have columns: Date, Open, High, Low, Close, Volume Date format in CSV: YYYY-MM-DD """