import glob csv_files = glob.glob('C:/CSVs/*.csv') for i, csv_file in enumerate(csv_files): security_name = os.path.basename(csv_file).replace('.csv', '') dat_filename = f'Fi+1:05d.DAT' # F00001.DAT, F00002.DAT, etc. csv_to_metastock(csv_file, 'C:/MetaStock/BatchData', security_name)
# Read and sort CSV data (reverse chronological) data = [] with open(csv_path, 'r') as f: reader = csv.DictReader(f) for row in reader: # Convert date from YYYY-MM-DD to YYYYMMDD integer date_obj = datetime.strptime(row['Date'], '%Y-%m-%d') date_int = int(date_obj.strftime('%Y%m%d')) # Convert values record = 'date': date_int, 'open': float(row['Open']), 'high': float(row['High']), 'low': float(row['Low']), 'close': float(row['Close']), 'volume': int(row['Volume']), 'open_interest': 0.0 # Default if not provided data.append(record)
# 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) convert csv to metastock 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 |
# Create output folder if not exists os.makedirs(output_folder, exist_ok=True) import glob csv_files = glob
# Reverse to MetaStock order (newest first) data.reverse()
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) import struct import os import csv from datetime
Once done, your CSV data will function exactly like native MetaStock data, allowing full charting, backtesting, and scanning.
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 """
Part 2: Required CSV Format Your CSV must contain these columns (exact names not required, but data is):
# 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