diff --git a/app/cron_swap.py b/app/cron_swap.py
new file mode 100644
index 0000000..93a784e
--- /dev/null
+++ b/app/cron_swap.py
@@ -0,0 +1,128 @@
+import pandas as pd
+import numpy as np
+import glob
+import requests
+import os
+from zipfile import ZipFile
+import datetime
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from tqdm import tqdm
+
+# Define some configuration variables
+OUTPUT_PATH = r"./json/swap"
+COMPANIES_PATH = r"./json/swap/companies"
+MAX_WORKERS = 1
+CHUNK_SIZE = 1000  # Adjust this value based on your system's RAM
+executor = ThreadPoolExecutor(max_workers=MAX_WORKERS)
+
+# Ensure the companies directory exists
+os.makedirs(COMPANIES_PATH, exist_ok=True)
+
+# List of stock symbols you're interested in
+stock_symbols = ['AAPL', 'GME', 'AMD']  # Add more symbols as needed
+
+
+start = datetime.datetime.today() - datetime.timedelta(days=30)
+end = datetime.datetime.today()
+dates = [start + datetime.timedelta(days=i) for i in range((end - start).days + 1)]
+
+# Generate filenames for each date
+filenames = [
+    f"SEC_CUMULATIVE_EQUITIES_{year}_{month}_{day}.zip"
+    for year, month, day in [
+        (date.strftime("%Y"), date.strftime("%m"), date.strftime("%d"))
+        for date in dates
+    ]
+]
+
+
+def download_and_process(filename):
+    url = f"https://pddata.dtcc.com/ppd/api/report/cumulative/sec/{filename}"
+    req = requests.get(url)
+    if req.status_code != 200:
+        print(f"Failed to download {url}")
+        return
+    
+    with open(filename, "wb") as f:
+        f.write(req.content)
+    
+    with ZipFile(filename, "r") as zip_ref:
+        csv_filename = zip_ref.namelist()[0]
+        zip_ref.extractall()
+    
+    output_filename = os.path.join(OUTPUT_PATH, f"{csv_filename}")
+    
+    # Process the CSV in chunks
+    chunk_list = []
+    for chunk in pd.read_csv(csv_filename, chunksize=CHUNK_SIZE, low_memory=False, on_bad_lines="skip"):
+        chunk_list.append(chunk)
+    
+    # Concatenate chunks and save
+    pd.concat(chunk_list, ignore_index=True).to_csv(output_filename, index=False)
+    
+    # Delete original downloaded files
+
+    os.remove(filename)
+    os.remove(csv_filename)
+
+tasks = []
+for filename in filenames:
+    tasks.append(executor.submit(download_and_process, filename))
+
+for task in tqdm(as_completed(tasks), total=len(tasks)):
+    pass
+
+files = glob.glob(OUTPUT_PATH + "/" + "*")
+
+def process_and_save_by_ticker():
+    csv_files = glob.glob(os.path.join(OUTPUT_PATH, "*.csv"))
+    
+    # Initialize DataFrames for each stock symbol
+    stock_data = {symbol: pd.DataFrame() for symbol in stock_symbols}
+    
+    for file in tqdm(csv_files, desc="Processing files"):
+        if not os.path.isfile(file):  # Skip if not a file
+            continue
+        try:
+            for chunk in pd.read_csv(file, chunksize=CHUNK_SIZE, low_memory=False, on_bad_lines="skip"):
+                if chunk.empty:
+                    continue
+                if "Dissemination Identifier" not in chunk.columns:
+                    chunk.rename(columns={
+                        "Dissemintation ID": "Dissemination Identifier",
+                        "Original Dissemintation ID": "Original Dissemination Identifier"
+                    }, inplace=True)
+                
+                # Filter and append data for each stock symbol
+                for symbol in stock_symbols:
+                    if "Primary Asset Class" in chunk.columns or "Action Type" in chunk.columns:
+                        symbol_data = chunk[chunk["Underlying Asset ID"].str.contains(f"{symbol}.", na=False)]
+                    else:
+                        symbol_data = chunk[chunk["Underlier ID-Leg 1"].str.contains(f"{symbol}.", na=False)]
+                    
+                    stock_data[symbol] = pd.concat([stock_data[symbol], symbol_data], ignore_index=True)
+                
+        except pd.errors.EmptyDataError:
+            print(f"Skipping empty file: {file}")
+        except Exception as e:
+            print(f"Error processing file {file}: {str(e)}")
+    
+    # Save data for each stock symbol
+    for symbol, data in stock_data.items():
+        if not data.empty:
+            # Treat "Original Dissemination Identifier" and "Dissemination Identifier" as long integers
+            data["Original Dissemination Identifier"] = data["Original Dissemination Identifier"].astype("Int64")
+            data["Dissemination Identifier"] = data["Dissemination Identifier"].astype("Int64")
+            data = data.drop(columns=["Unnamed: 0"], errors="ignore")
+            
+            # Keep only specific columns
+            columns_to_keep = ["Effective Date", "Notional amount-Leg 1", "Expiration Date", "Total notional quantity-Leg 1"]
+            data = data[columns_to_keep]
+
+            # Save to CSV
+            output_file = os.path.join(COMPANIES_PATH, f"{symbol}.csv")
+            data.to_csv(output_file, index=False)
+            print(f"Saved data for {symbol} to {output_file}")
+
+
+process_and_save_by_ticker()
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