logic.py

# ---------------- LOGIC ----------------
# This file contains all the data processing and simulation logic.

import pandas as pd
import numpy as np
import yfinance as yf
import plotly.express as px
import plotly.graph_objects as go
import matplotlib.pyplot as plt
import warnings
from datetime import timedelta

# Import configuration variables
from config import (
    CRISIS_PERIODS,
    BENCHMARK_TICKER,
    BENCHMARK_NAME,
    RECOVERY_DAYS,
    CRISIS_SUMMARY,
    CRISIS_INSIGHTS,
    GEMINI_MODEL_NAME,
    GEMINI_SYSTEM_PROMPT,
    GEMINI_USER_PROMPT_TEMPLATE,
)

warnings.filterwarnings("ignore")

try:
    import google.generativeai as genai
except ImportError:
    genai = None

# ---------------- UTILS ----------------

def _ensure_ns_suffix(t):
    """Ensures a ticker has the .NS suffix for Indian stocks."""
    t = t.strip().upper()
    if t.startswith("^") or "." in t:
        return t
    return t + ".NS"

def _fetch_prices(tickers, start, end):
    """Fetches historical price data from yfinance."""
    raw = yf.download(tickers, start=start, end=end, progress=False, auto_adjust=True)
    if "Adj Close" in raw:
        df = raw["Adj Close"]
    elif "Close" in raw:
        df = raw["Close"]
    else:
        df = raw
    if isinstance(df, pd.Series):
        df = df.to_frame()
    # Handle single ticker download which doesn't have multi-index cols
    if not isinstance(df.columns, pd.MultiIndex):
        df.columns = [c.upper() for c in df.columns]
    return df

def calc_metrics(series, benchmark_returns=None):
    """Calculates key performance metrics for a time series."""
    returns = series.pct_change().dropna()
    if returns.empty:
        return {
            "total_return": 0,
            "volatility": 0,
            "VaR_95": 0,
            "CAGR": 0,
            "max_drawdown": 0,
            "beta": None,
        }
    
    total_return = (series.iloc[-1] / series.iloc[0]) - 1
    vol = returns.std() * np.sqrt(252)
    VaR_95 = returns.quantile(0.05)
    days = (series.index[-1] - series.index[0]).days
    years = max(days / 365.25, 1 / 365.25)
    CAGR = (series.iloc[-1] / series.iloc[0]) ** (1 / years) - 1
    drawdown = (series / series.cummax()) - 1
    max_dd = drawdown.min()
    beta = None
    if benchmark_returns is not None:
        rr, br = returns.align(benchmark_returns, join="inner")
        if len(rr) > 10:
            cov = np.cov(rr, br)[0, 1]
            varb = np.var(br)
            beta = cov / varb if varb != 0 else np.nan
    return {
        "total_return": total_return,
        "volatility": vol,
        "VaR_95": VaR_95,
        "CAGR": CAGR,
        "max_drawdown": max_dd,
        "beta": beta,
    }

def sector_from_ticker(t):
    """Fetches sector and industry info for a ticker."""
    try:
        info = yf.Ticker(t).info
        return info.get("sector", "Unknown"), info.get("industry", "Unknown")
    except Exception:
        return "Unknown", "Unknown"

def format_pct(x):
    """Formats a float as a percentage string."""
    if x is None or (isinstance(x, float) and np.isnan(x)):
        return "N/A"
    return f"{x * 100:.2f}%"

# ---------------- GEMINI AI HELPER ----------------

def generate_gemini_insights(

    api_key: str,

    crisis_name: str,

    metrics_md: str,

    extra_instructions: str = "",

) -> str:
    """Call Gemini to get AI-generated insights based on metrics."""
    if not api_key:
        return "ℹ️ To see AI-generated insights, please paste a valid Gemini API key."
    
    if genai is None:
        return "⚠️ google-generativeai is not installed. Run `pip install google-generativeai` and retry."
    
    user_prompt = GEMINI_USER_PROMPT_TEMPLATE.format(
        crisis_name=crisis_name,
        metrics_text=metrics_md,
        extra_instructions=extra_instructions or "None.",
    )
    
    try:
        genai.configure(api_key=api_key)
        model = genai.GenerativeModel(
            GEMINI_MODEL_NAME,
            system_instruction=GEMINI_SYSTEM_PROMPT.strip(),
            generation_config={"max_output_tokens": 256},
        )
        response = model.generate_content(user_prompt)
        text = getattr(response, "text", "") or ""
        if not text.strip():
            return "⚠️ Gemini did not return any text. Please check your API key, quota, or try again."
        return text.strip()
    except Exception as e:
        return f"⚠️ Gemini call failed: {e}"

# ---------------- SIMULATION ----------------

def run_crisis_simulation(

    crisis,

    uploaded,

    tickers_str,

    weights_str,

    include_etf,

    gemini_api_key="",

    gemini_extra_prompt="",

):
    """

    The main simulation function.

    Takes user inputs, processes the portfolio, fetches data,

    and returns all outputs for the Gradio interface.

    """
    
    # --- 1. Parse Portfolio ---
    if uploaded is not None:
        try:
            df = pd.read_csv(uploaded.name if hasattr(uploaded, "name") else uploaded)
        except Exception as e:
            return (
                None,
                f"Error reading CSV: {e}",
                None,
                None,
                None,
                "",
                "No AI insights (CSV error).",
            )
    else:
        try:
            tickers = [t.strip() for t in tickers_str.split(",") if t.strip()]
            weights = [float(w) for w in weights_str.split(",") if w.strip()]
            if not tickers or not weights or len(tickers) != len(weights):
                return (
                    None,
                    "Error: Mismatch between tickers and weights, or fields are empty.",
                    None,
                    None,
                    None,
                    "",
                    "No AI insights (input mismatch).",
                )
            df = pd.DataFrame({"Ticker": tickers, "Weight": weights})
        except ValueError:
            return (
                None,
                "Error: Weights must be numbers.",
                None,
                None,
                None,
                "",
                "No AI insights (weights error).",
            )

    if df.empty or "Ticker" not in df or "Weight" not in df:
        return (
            None,
            "Error: Invalid portfolio. Check inputs.",
            None,
            None,
            None,
            "",
            "No AI insights (invalid portfolio).",
        )

    df["Ticker"] = df["Ticker"].apply(_ensure_ns_suffix)
    
    # --- 2. Normalize Weights (with ETF logic) ---
    try:
        if include_etf:
            # Scale user's portfolio to 95%
            df["Weight"] = (
                df["Weight"].astype(float) / df["Weight"].astype(float).sum()
            ) * 0.95
            # Add the 5% ETF
            etf_row = pd.DataFrame([{"Ticker": "NIFTYBEES.NS", "Weight": 0.05}])
            df = pd.concat([df, etf_row], ignore_index=True)
        else:
            # Normalize user's portfolio to 100%
            df["Weight"] = df["Weight"].astype(float) / df["Weight"].astype(float).sum()
    except ZeroDivisionError:
        return (
            None,
            "Error: Portfolio weights sum to zero.",
            None,
            None,
            None,
            "",
            "No AI insights (weights zero).",
        )

    # --- 3. Fetch Data ---
    start, end = CRISIS_PERIODS[crisis]
    recovery_end = pd.to_datetime(end) + pd.Timedelta(days=RECOVERY_DAYS)
    
    tickers = list(df["Ticker"].unique()) + [BENCHMARK_TICKER]
    
    prices = _fetch_prices(tickers, start, recovery_end)
    if prices.empty:
        return (
            None,
            "No data found. Some tickers may not exist historically.",
            None,
            None,
            None,
            "",
            "No AI insights (no data).",
        )
    
    # Ensure all required tickers were fetched
    fetched_tickers = [c.upper() for c in prices.columns]
    required_tickers = [t.upper() for t in df["Ticker"]] + [BENCHMARK_TICKER.upper()]
    
    missing = [t for t in required_tickers if t not in fetched_tickers]
    if missing:
        return (
            None,
            f"Error: Could not fetch data for: {', '.join(missing)}",
            None,
            None,
            None,
            "",
            "No AI insights (missing tickers).",
        )

    prices.ffill(inplace=True)
    crisis_window = prices.loc[start:end]
    
    if BENCHMARK_TICKER not in crisis_window.columns:
        return (
            None,
            f"Error: Could not fetch benchmark {BENCHMARK_NAME} data for this period.",
            None,
            None,
            None,
            "",
            "No AI insights (benchmark error).",
        )
    
    bench = crisis_window[BENCHMARK_TICKER]
    
    # --- 4. Calculate Portfolio Performance ---
    df_aligned = df.set_index("Ticker")
    df_aligned.index = df_aligned.index.str.upper()
    
    # Filter price columns to only those in our portfolio
    portfolio_prices = crisis_window[df_aligned.index]
    
    norm = (portfolio_prices / portfolio_prices.iloc[0]) * 100
    weighted = (norm * df_aligned["Weight"]).sum(axis=1)
    weighted.name = "Portfolio"
    bench_norm = (bench / bench.iloc[0]) * 100

    port_m = calc_metrics(weighted, bench.pct_change())
    bench_m = calc_metrics(bench_norm)

    # --- 5. Generate Outputs (Metrics Table) ---
    beta_val = port_m["beta"]
    if beta_val is None or (isinstance(beta_val, float) and np.isnan(beta_val)):
        beta_str = "N/A"
    else:
        beta_str = f"{beta_val:.2f}"

    metrics_md = f"""### Simulation: {crisis}

| Metric | Portfolio | {BENCHMARK_NAME} |

|:---|---:|---:|

| **Total Return** | **{format_pct(port_m['total_return'])}** | **{format_pct(bench_m['total_return'])}** |

| Max Drawdown | {format_pct(port_m['max_drawdown'])} | {format_pct(bench_m['max_drawdown'])} |

| Volatility (Ann.) | {format_pct(port_m['volatility'])} | {format_pct(bench_m['volatility'])} |

| CAGR | {format_pct(port_m['CAGR'])} | {format_pct(bench_m['CAGR'])} |

| Beta | {beta_str} | - |

| VaR (95%, Daily) | {format_pct(port_m['VaR_95'])} | {format_pct(bench_m['VaR_95'])} |

"""

    # --- 6. Generate Outputs (Performance Plot) ---
    fig = go.Figure()
    fig.add_trace(
        go.Scatter(
            x=weighted.index,
            y=weighted.values,
            name="Portfolio",
            mode="lines",
            line=dict(width=3, color="#1E88E5"),
        )
    )
    fig.add_trace(
        go.Scatter(
            x=bench_norm.index,
            y=bench_norm.values,
            name=BENCHMARK_NAME,
            mode="lines",
            line=dict(width=2, color="#FFC107", dash="dot"),
        )
    )
    fig.update_layout(
        title=f"<b>{crisis}</b>: Portfolio vs Benchmark Performance",
        template="plotly_white",
        xaxis_title="Date",
        yaxis_title="Normalized Value (Base 100)",
        height=450,
        legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01),
    )

    # --- 7. Generate Outputs (Sector Analysis) ---
    df["Sector"], df["Industry"] = zip(*df["Ticker"].map(sector_from_ticker))
    sector_dd = []
    for t in df.Ticker:
        if t.upper() in crisis_window.columns:
            ser = crisis_window[t.upper()]
            dd = (ser / ser.cummax() - 1).min()
            sector_dd.append(dd)
        else:
            sector_dd.append(0)  # Ticker wasn't in crisis window
            
    df["Drawdown"] = sector_dd
    
    # Aggregate weighted drawdown by sector
    sec_agg = df.groupby("Sector").apply(
        lambda d: np.average(d["Drawdown"], weights=d["Weight"] / d["Weight"].sum())
    )
    sec_agg = sec_agg.sort_values()

    sec_fig = px.bar(
        sec_agg * 100,
        y=sec_agg.index,
        x=sec_agg.values,
        orientation="h",
        title="Weighted Max Drawdown by Sector",
        labels={"x": "Max Drawdown (%)", "y": "Sector"},
    )
    sec_fig.update_layout(
        template="plotly_white",
        yaxis={"categoryorder": "total ascending"},
    )

    # --- 8. Generate Outputs (Insights & Pie Chart) ---
    ins = [
        f"### Insights for: {crisis}",
        f"_{CRISIS_SUMMARY.get(crisis, 'No summary available.')}_",
    ]
    if crisis in CRISIS_INSIGHTS:
        for s, txt in CRISIS_INSIGHTS[crisis].items():
            ins.append(f"- **{s}**: {txt}")
    insights_md = "\n".join(ins)
    
    # --- 8. Generate Outputs (Insights & Pie Chart) ---
    ins = [
        f"### Insights for: {crisis}",
        f"_{CRISIS_SUMMARY.get(crisis, 'No summary available.')}_",
    ]
    if crisis in CRISIS_INSIGHTS:
        for s, txt in CRISIS_INSIGHTS[crisis].items():
            ins.append(f"- **{s}**: {txt}")
    insights_md = "\n".join(ins)

    # --- 8. Generate Outputs (Insights & Pie Chart) ---
    ins = [
        f"### Insights for: {crisis}",
        f"_{CRISIS_SUMMARY.get(crisis, 'No summary available.')}_",
    ]
    if crisis in CRISIS_INSIGHTS:
        for s, txt in CRISIS_INSIGHTS[crisis].items():
            ins.append(f"- **{s}**: {txt}")
    insights_md = "\n".join(ins)

    # --- 8. Generate Outputs (Insights & Pie Chart) ---
    ins = [
        f"### Insights for: {crisis}",
        f"_{CRISIS_SUMMARY.get(crisis, 'No summary available.')}_",
    ]
    if crisis in CRISIS_INSIGHTS:
        for s, txt in CRISIS_INSIGHTS[crisis].items():
            ins.append(f"- **{s}**: {txt}")
    insights_md = "\n".join(ins)

    # --- Pie chart: final portfolio weights (including ETF if added) ---
    pie_df = df[["Ticker", "Weight"]].copy()
    pie_df["Ticker"] = pie_df["Ticker"].astype(str)
    pie_df["Weight"] = pd.to_numeric(pie_df["Weight"], errors="raise")

    wsum = pie_df["Weight"].sum()
    if wsum <= 0:
        raise ValueError(f"Pie chart error: portfolio weights sum to {wsum}.")
    pie_df["Weight"] = pie_df["Weight"] / wsum

    print("DEBUG pie_df for pie chart:\n", pie_df)
    print("DEBUG weight sum:", pie_df["Weight"].sum())

    # Matplotlib pie chart
    fig_pie, ax = plt.subplots(figsize=(4, 4))
    ax.pie(
        pie_df["Weight"].values,
        labels=pie_df["Ticker"].values,
        autopct="%1.1f%%", 
        startangle=90,
    )
    ax.set_title("Final Portfolio Allocation")
    ax.axis("equal")

    # --- 9. Logs & AI Insights ---
    log_message = f"✅ Simulation Complete. Received weights: '{weights_str}'"

    gemini_insights = generate_gemini_insights(
        api_key=gemini_api_key or "",
        crisis_name=crisis,
        metrics_md=metrics_md,
        extra_instructions=gemini_extra_prompt or "",
    )

    return fig, metrics_md, sec_fig, insights_md, fig_pie, log_message, gemini_insights