Initial commit: Touch & Turn Scalping Bot with fully automated execution, backtesting, and ISA screening

2026-04-22 21:19:33 +01:00
commit dc111abf8c
15 changed files with 1518 additions and 0 deletions
@@ -0,0 +1,60 @@
+import os
+import logging
+import json
+import pandas as pd
+from dotenv import load_dotenv
+from src.api.client import Trading212Client
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+def fetch_and_save_tickers():
+    load_dotenv()
+    api_key_id = os.getenv("TRADING212_API_KEY_ID")
+    api_key = os.getenv("TRADING212_API_KEY")
+    base_url = os.getenv("TRADING212_BASE_URL", "https://demo.trading212.com/api/v0/")
+
+    if not api_key_id or not api_key:
+        logger.error("API Key ID or API Key is missing in .env")
+        return
+
+    client = Trading212Client(api_key_id, api_key, base_url)
+    
+    try:
+        logger.info("Fetching available instruments from Trading212...")
+        instruments = client.get_instruments()
+        
+        logger.info(f"Retrieved {len(instruments)} instruments.")
+        
+        # Save raw JSON
+        with open("available_instruments.json", "w") as f:
+            json.dump(instruments, f, indent=4)
+        logger.info("Saved raw data to available_instruments.json")
+        
+        # Convert to a DataFrame and save as CSV for easier viewing/filtering
+        df = pd.DataFrame(instruments)
+        
+        # Select the most useful columns if they exist
+        expected_columns = ['ticker', 'name', 'type', 'currencyCode', 'exchange']
+        available_columns = [col for col in expected_columns if col in df.columns]
+        
+        if available_columns:
+            df_filtered = df[available_columns]
+            
+            # Print a quick summary of US Equities (which is what Touch & Turn trades)
+            if 'type' in df.columns and 'currencyCode' in df.columns:
+                us_stocks = df[(df['type'] == 'STOCK') & (df['currencyCode'] == 'USD')]
+                logger.info(f"Found {len(us_stocks)} US Stocks.")
+                logger.info("\nSample of US Stocks available:")
+                print(us_stocks.head(10).to_string(index=False))
+        else:
+            df_filtered = df
+            
+        df_filtered.to_csv("available_tickers.csv", index=False)
+        logger.info("Saved structured list to available_tickers.csv")
+        
+    except Exception as e:
+        logger.error(f"Failed to fetch instruments: {e}")
+
+if __name__ == "__main__":
+    fetch_and_save_tickers()