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

src/strategy/scanner.py

@@ -0,0 +1,77 @@
+import yfinance as yf
+import pandas_ta as ta
+import pandas as pd
+import logging
+from typing import List, Dict
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+DEFAULT_TICKERS = [
+    "AAPL", "MSFT", "NVDA", "AMZN", "META", "GOOGL", "TSLA",
+    "AMD", "NFLX", "QCOM", "INTC", "BA", "DIS", "SPY", "QQQ"
+]
+
+def scan_for_candidates(tickers: List[str] = DEFAULT_TICKERS, min_price: float = 20.0, min_volume: int = 2_000_000) -> pd.DataFrame:
+    """
+    Scans a list of tickers to find the best candidates for the Touch & Turn strategy.
+    Prioritizes high Average True Range (ATR) as a percentage of price, ensuring adequate volume.
+    """
+    logger.info(f"Scanning {len(tickers)} tickers for high volatility/liquidity...")
+    results = []
+
+    # Download daily data for the past 1mo to calculate 14-day ATR and Avg Volume
+    data = yf.download(tickers, period="1mo", interval="1d", group_by="ticker", progress=False)
+
+    for ticker in tickers:
+        try:
+            # Handle single ticker vs multi-ticker dataframe structure from yfinance
+            if len(tickers) == 1:
+                df = data.copy()
+            else:
+                df = data[ticker].copy()
+
+            if df.empty or len(df) < 15:
+                continue
+            
+            # Clean column names (yfinance multi-index can sometimes leave tuple names)
+            if isinstance(df.columns, pd.MultiIndex):
+                df.columns = df.columns.droplevel(1)
+                
+            df.ta.atr(length=14, append=True)
+            
+            latest = df.iloc[-1]
+            yesterday_atr = df['ATRr_14'].iloc[-2]
+            
+            close_price = latest['Close']
+            avg_volume = df['Volume'].tail(14).mean()
+            
+            # Filters
+            if close_price < min_price or avg_volume < min_volume or pd.isna(yesterday_atr):
+                continue
+                
+            atr_percent = (yesterday_atr / close_price) * 100
+            
+            results.append({
+                "Ticker": ticker,
+                "Close": round(float(close_price), 2),
+                "ATR_14": round(float(yesterday_atr), 2),
+                "ATR_Percent": round(float(atr_percent), 2),
+                "Avg_Volume": int(avg_volume)
+            })
+            
+        except Exception as e:
+            logger.debug(f"Failed processing {ticker}: {e}")
+
+    results_df = pd.DataFrame(results)
+    if not results_df.empty:
+        # Sort by ATR Percentage descending (we want the most volatile stocks)
+        results_df = results_df.sort_values(by="ATR_Percent", ascending=False).reset_index(drop=True)
+        
+    return results_df
+
+if __name__ == "__main__":
+    candidates = scan_for_candidates()
+    print("\nTop Candidates for Touch & Turn Strategy:")
+    print("-" * 65)
+    print(candidates.to_string(index=False))