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"""Data fetching and processing utilities for the financial dashboard.""" |
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import pandas as pd |
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from openbb import sdk |
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def load_stock_data(symbol: str) -> pd.DataFrame: |
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"""Load historical stock price data.""" |
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df = sdk.equity.price.historical(symbol=symbol).to_dataframe() |
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return df |
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def load_company_profile(symbol: str): |
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"""Load company profile information.""" |
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profile_response = sdk.equity.profile(symbol=symbol) |
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profile_info = profile_response.results[0] if hasattr(profile_response, 'results') and profile_response.results else None |
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return profile_info |
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def load_income_statement(symbol: str) -> pd.DataFrame: |
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"""Load company income statement data.""" |
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income_stmt = sdk.equity.fundamental.income(symbol=symbol).to_dataframe() |
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return income_stmt |
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def calculate_technical_indicators(df: pd.DataFrame, period: int) -> pd.DataFrame: |
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"""Calculate SMA, EMA, and RSI indicators.""" |
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df["SMA"] = df["close"].rolling(period).mean() |
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df["EMA"] = df["close"].ewm(span=period, adjust=False).mean() |
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delta = df["close"].diff() |
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gain = delta.clip(lower=0) |
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loss = -1 * delta.clip(upper=0) |
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avg_gain = gain.rolling(period).mean() |
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avg_loss = loss.rolling(period).mean() |
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rs = avg_gain / avg_loss |
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df["RSI"] = 100 - (100 / (1 + rs)) |
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return df |
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def format_financial_value(value) -> str: |
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"""Format financial values with appropriate units.""" |
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if pd.isna(value): |
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return "N/A" |
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if abs(value) >= 1e9: |
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return f"${value/1e9:.2f}B" |
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elif abs(value) >= 1e6: |
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return f"${value/1e6:.2f}M" |
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else: |
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return f"${value:.2f}" |
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def get_price_metrics(df: pd.DataFrame) -> dict: |
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"""Calculate key price metrics.""" |
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current_price = df["close"].iloc[-1] |
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prev_close = df["close"].iloc[-2] if len(df) > 1 else df["close"].iloc[0] |
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price_change = current_price - prev_close |
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price_change_pct = (price_change / prev_close) * 100 if prev_close != 0 else 0 |
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return { |
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"current_price": current_price, |
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"price_change": price_change, |
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"price_change_pct": price_change_pct, |
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"high_52w": df['high'].max(), |
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"low_52w": df['low'].min(), |
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} |
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def get_profitability_metrics(income_data: pd.Series) -> dict: |
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"""Calculate profitability metrics from income statement.""" |
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total_rev = income_data.get('total_revenue', 0) |
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gross_prof = income_data.get('gross_profit', 0) |
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net_inc = income_data.get('net_income', 0) |
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operating_inc = income_data.get('operating_income', 0) |
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metrics = {} |
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if total_rev and total_rev > 0: |
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metrics["gross_margin"] = (gross_prof / total_rev) * 100 if pd.notna(gross_prof) else 0 |
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metrics["net_margin"] = (net_inc / total_rev) * 100 if pd.notna(net_inc) else 0 |
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if operating_inc: |
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metrics["operating_margin"] = (operating_inc / total_rev) * 100 |
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else: |
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metrics = {"gross_margin": 0, "net_margin": 0} |
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return metrics |
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