| File | Description | |-------|-------------| | MASTER | An index file containing all security names and their properties. | | EMASTER | Extended master file for additional fields (optional). | | F<nnnn>.DAT | The actual price data file (e.g., F00001.DAT ). |
Once done, your CSV data will function exactly like native MetaStock data, allowing full charting, backtesting, and scanning. convert csv to metastock format
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 """ | File | Description | |-------|-------------| | MASTER
# 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 | Once done, your CSV data will function
# Create output folder if not exists os.makedirs(output_folder, exist_ok=True)
| 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 |