!`python3 -c "exec('try:\n import yfinance, pandas, numpy\n print(f\'yfinance={yfinance.__version__} pandas={pandas.__version__} numpy={numpy.__version__}\')\nexcept Exception:\n print(\'DEPS_MISSING\')')"`

import subprocess, sys
subprocess.check_call([sys.executable, "-m", "pip", "install", "-q", "yfinance", "pandas", "numpy"])

import yfinance as yf
import pandas as pd
import numpy as np

def liquidity_dashboard(ticker_symbol, period="3mo"):
    ticker = yf.Ticker(ticker_symbol)
    info = ticker.info
    hist = ticker.history(period=period)

    if hist.empty:
        return None

    # --- Spread metrics (from current quote) ---
    bid = info.get("bid", None)
    ask = info.get("ask", None)
    current_price = info.get("currentPrice") or info.get("regularMarketPrice") or hist["Close"].iloc[-1]

    spread = None
    spread_pct = None
    if bid and ask and bid > 0 and ask > 0:
        spread = round(ask - bid, 4)
        midpoint = (ask + bid) / 2
        spread_pct = round((spread / midpoint) * 100, 4)

    # --- Volume metrics ---
    avg_volume = hist["Volume"].mean()
    median_volume = hist["Volume"].median()
    avg_dollar_volume = (hist["Close"] * hist["Volume"]).mean()
    volume_std = hist["Volume"].std()
    volume_cv = volume_std / avg_volume if avg_volume > 0 else None  # coefficient of variation

    # --- Turnover ratio ---
    shares_outstanding = info.get("sharesOutstanding", None)
    float_shares = info.get("floatShares", None)
    base_shares = float_shares or shares_outstanding
    turnover_ratio = round(avg_volume / base_shares, 6) if base_shares else None

    # --- Amihud illiquidity ratio ---
    # Average of |daily return| / daily dollar volume
    returns = hist["Close"].pct_change().dropna()
    dollar_volume = (hist["Close"] * hist["Volume"]).iloc[1:]  # align with returns
    amihud_values = returns.abs() / dollar_volume
    amihud = amihud_values[amihud_values.replace([np.inf, -np.inf], np.nan).notna()].mean()

    # --- Market impact estimate (square-root model) ---
    # For a hypothetical order of 1% of ADV
    adv = avg_volume
    order_size = adv * 0.01
    daily_volatility = returns.std()
    sigma = daily_volatility
    participation_rate = order_size / adv if adv > 0 else 0
    impact_bps = sigma * np.sqrt(participation_rate) * 10000  # in basis points

    return {
        "ticker": ticker_symbol,
        "current_price": round(current_price, 2),
        "bid": bid,
        "ask": ask,
        "spread": spread,
        "spread_pct": spread_pct,
        "avg_daily_volume": int(avg_volume),
        "median_daily_volume": int(median_volume),
        "avg_dollar_volume": round(avg_dollar_volume, 0),
        "volume_cv": round(volume_cv, 3) if volume_cv else None,
        "shares_outstanding": shares_outstanding,
        "float_shares": float_shares,
        "turnover_ratio": turnover_ratio,
        "amihud_illiquidity": round(amihud * 1e9, 4) if not np.isnan(amihud) else None,
        "daily_volatility": round(daily_volatility * 100, 2),
        "impact_1pct_adv_bps": round(impact_bps, 2),
        "observations": len(hist),
    }

import yfinance as yf

def spread_analysis(ticker_symbol):
    ticker = yf.Ticker(ticker_symbol)
    info = ticker.info

    bid = info.get("bid", 0)
    ask = info.get("ask", 0)
    bid_size = info.get("bidSize", None)
    ask_size = info.get("askSize", None)
    current_price = info.get("currentPrice") or info.get("regularMarketPrice", 0)

    result = {"bid": bid, "ask": ask, "bid_size": bid_size, "ask_size": ask_size}

    if bid > 0 and ask > 0:
        midpoint = (bid + ask) / 2
        result["absolute_spread"] = round(ask - bid, 4)
        result["relative_spread_pct"] = round((ask - bid) / midpoint * 100, 4)
        result["relative_spread_bps"] = round((ask - bid) / midpoint * 10000, 2)
    return result

import yfinance as yf
import pandas as pd
import numpy as np

def volume_analysis(ticker_symbol, period="3mo"):
    ticker = yf.Ticker(ticker_symbol)
    hist = ticker.history(period=period)

    if hist.empty:
        return None

    vol = hist["Volume"]
    close = hist["Close"]
    dollar_vol = vol * close

    # Relative volume (today vs average)
    rvol = vol.iloc[-1] / vol.mean() if vol.mean() > 0 else None

    # Volume trend (linear regression slope over the period)
    x = np.arange(len(vol))
    slope, _ = np.polyfit(x, vol.values, 1) if len(vol) > 1 else (0, 0)
    trend_pct = (slope * len(vol)) / vol.mean() * 100  # % change over period

    # Volume profile by day of week
    hist_copy = hist.copy()
    hist_copy["DayOfWeek"] = hist_copy.index.dayofweek
    day_names = {0: "Mon", 1: "Tue", 2: "Wed", 3: "Thu", 4: "Fri"}
    vol_by_day = hist_copy.groupby("DayOfWeek")["Volume"].mean()
    vol_by_day.index = vol_by_day.index.map(day_names)

    # High/low volume days
    high_vol_days = hist.nlargest(5, "Volume")[["Close", "Volume"]]
    low_vol_days = hist.nsmallest(5, "Volume")[["Close", "Volume"]]

    return {
        "avg_volume": int(vol.mean()),
        "median_volume": int(vol.median()),
        "avg_dollar_volume": round(dollar_vol.mean(), 0),
        "current_volume": int(vol.iloc[-1]),
        "relative_volume": round(rvol, 2) if rvol else None,
        "volume_trend_pct": round(trend_pct, 1),
        "volume_by_day": vol_by_day.to_dict(),
        "high_vol_days": high_vol_days,
        "low_vol_days": low_vol_days,
        "max_volume": int(vol.max()),
        "min_volume": int(vol.min()),
    }

import yfinance as yf
import numpy as np

def market_impact(ticker_symbol, order_shares=None, order_dollars=None, period="3mo"):
    ticker = yf.Ticker(ticker_symbol)
    hist = ticker.history(period=period)
    info = ticker.info

    if hist.empty:
        return None

    current_price = info.get("currentPrice") or hist["Close"].iloc[-1]
    avg_volume = hist["Volume"].mean()
    daily_volatility = hist["Close"].pct_change().dropna().std()

    # Determine order size in shares
    if order_dollars and not order_shares:
        order_shares = order_dollars / current_price
    elif not order_shares:
        # Default: estimate for various sizes
        order_shares = avg_volume * 0.01  # 1% of ADV

    participation_rate = order_shares / avg_volume if avg_volume > 0 else 0
    pct_adv = (order_shares / avg_volume * 100) if avg_volume > 0 else 0

    # Square-root impact model
    impact_pct = daily_volatility * np.sqrt(participation_rate) * 100
    impact_bps = impact_pct * 100
    impact_dollars = impact_pct / 100 * current_price * order_shares

    # Generate impact curve for multiple order sizes
    sizes = [0.001, 0.005, 0.01, 0.02, 0.05, 0.10, 0.20, 0.50]  # as fraction of ADV
    curve = []
    for s in sizes:
        q = avg_volume * s
        imp = daily_volatility * np.sqrt(s) * 100
        curve.append({
            "pct_adv": round(s * 100, 1),
            "shares": int(q),
            "dollars": round(q * current_price, 0),
            "impact_bps": round(imp * 100, 1),
            "impact_dollars_per_share": round(imp / 100 * current_price, 4),
        })

    return {
        "ticker": ticker_symbol,
        "current_price": round(current_price, 2),
        "avg_daily_volume": int(avg_volume),
        "daily_volatility_pct": round(daily_volatility * 100, 2),
        "order_shares": int(order_shares),
        "order_dollars": round(order_shares * current_price, 0),
        "pct_of_adv": round(pct_adv, 2),
        "estimated_impact_bps": round(impact_bps, 1),
        "estimated_impact_pct": round(impact_pct, 4),
        "estimated_impact_total_dollars": round(impact_dollars, 2),
        "impact_curve": curve,
    }

import yfinance as yf
import pandas as pd
import numpy as np

def turnover_analysis(ticker_symbol, period="3mo"):
    ticker = yf.Ticker(ticker_symbol)
    hist = ticker.history(period=period)
    info = ticker.info

    if hist.empty:
        return None

    avg_volume = hist["Volume"].mean()
    shares_outstanding = info.get("sharesOutstanding")
    float_shares = info.get("floatShares")

    result = {
        "avg_daily_volume": int(avg_volume),
        "shares_outstanding": shares_outstanding,
        "float_shares": float_shares,
    }

    if shares_outstanding:
        daily_turnover = avg_volume / shares_outstanding
        result["daily_turnover_ratio"] = round(daily_turnover, 6)
        result["annualized_turnover"] = round(daily_turnover * 252, 2)
        result["days_to_trade_float"] = round(
            (float_shares or shares_outstanding) / avg_volume, 1
        ) if avg_volume > 0 else None

    if float_shares:
        float_turnover = avg_volume / float_shares
        result["float_turnover_daily"] = round(float_turnover, 6)
        result["float_turnover_annualized"] = round(float_turnover * 252, 2)

    # Turnover trend
    vol = hist["Volume"]
    base = float_shares or shares_outstanding
    if base:
        hist_copy = hist.copy()
        hist_copy["turnover"] = hist_copy["Volume"] / base
        recent_turnover = hist_copy["turnover"].tail(20).mean()
        older_turnover = hist_copy["turnover"].head(20).mean()
        if older_turnover > 0:
            result["turnover_trend_pct"] = round(
                (recent_turnover - older_turnover) / older_turnover * 100, 1
            )

    return result