A line-break band system that encodes directional strength using count-based alpha and ATR-driven volatility offsets

				
					# Data Download & Preprocessing

# Download daily data
df = yf.download(TICKER, start=START_DATE, end=END_DATE, interval="1d", auto_adjust=False)
if df.empty:
    print("No data returned for the given ticker/date range.")
    exit(1)

# Flatten multi-index columns if present
if isinstance(df.columns, pd.MultiIndex):
    df.columns = df.columns.map("_".join)

# Robust column renaming:
# Expected keys for price data.
keys = ["Open", "High", "Low", "Close", "Volume"]
rename_dict = {}
for key in keys:
    if key not in df.columns:
        # Search for any column that starts with the key
        for col in df.columns:
            if col.startswith(key):
                rename_dict[col] = key
                print(f"Column '{col}' found. Renaming it to '{key}'.")
                break
if rename_dict:
    df.rename(columns=rename_dict, inplace=True)

# Verify required columns exist now.
for key in ["High", "Low", "Close"]:
    if key not in df.columns:
        print(f"Required column '{key}' not found after renaming. Available columns: {df.columns}")
        exit(1)

df.dropna(inplace=True)
if df.empty:
    print("Data is empty after cleaning. Exiting.")
    exit()

# Standardize column names for later use.
df["Price"] = df["Close"]
df["Time"]  = df.index

# Convert the date index to a NumPy array (for positional indexing).
dates_arr = df.index.to_numpy()