use crate::analysis::{CategoryScore, Decision};
use crate::data::EtfData;

pub fn verdict(data: &EtfData, decision: &Decision) -> String {
    let name = data.fund_name.as_deref().unwrap_or(&data.symbol);
    format!("{} — {}", name, decision.label())
}

pub fn what_fund_does(data: &EtfData) -> String {
    let name = data.fund_name.as_deref().unwrap_or(&data.symbol);
    let category = data.category.as_deref().unwrap_or("diversified");

    if let Some(aum) = data.total_assets {
        format!(
            "{name} is a {category} ETF with ${:.1}B in assets under management.",
            aum / 1e9
        )
    } else {
        format!("{name} is a {category} ETF.")
    }
}

pub fn why_opinion(_data: &EtfData, scores: &[CategoryScore]) -> String {
    let mut drivers: Vec<(f64, &str)> = scores
        .iter()
        .filter(|s| s.score.abs() >= 1.0)
        .map(|s| (s.score * s.weight, s.detail.as_str()))
        .collect();
    drivers.sort_by(|a, b| b.0.abs().partial_cmp(&a.0.abs()).unwrap());

    let lines: Vec<String> = drivers.iter().take(3).map(|(_, d)| d.to_string()).collect();
    if lines.is_empty() {
        "No single dominant factor — score is borderline.".to_string()
    } else {
        lines.join(". ") + "."
    }
}

pub fn looks_good(_data: &EtfData, scores: &[CategoryScore]) -> Vec<String> {
    scores.iter().filter(|s| s.score > 0.0).map(|s| s.detail.clone()).collect()
}

pub fn worries(data: &EtfData, scores: &[CategoryScore]) -> Vec<String> {
    let mut items: Vec<String> = scores
        .iter()
        .filter(|s| s.score < 0.0)
        .map(|s| s.detail.clone())
        .collect();

    if data.is_leveraged {
        items.push("⚠ Leveraged ETF — not suitable for long-term holding".to_string());
    }
    if data.is_inverse {
        items.push("⚠ Inverse ETF — designed for short-term hedging only".to_string());
    }

    items
}

pub fn dividend_check(data: &EtfData) -> String {
    match data.dividend_yield {
        Some(y) if y > 0.0 => format!("Distribution yield: {:.2}%", y * 100.0),
        _ => "This ETF does not currently distribute dividends or yield data is unavailable.".to_string(),
    }
}

pub fn performance_check(data: &EtfData) -> String {
    let mut parts = Vec::new();
    if let Some(ytd) = data.ytd_return {
        parts.push(format!("YTD: {:.1}%", ytd));
    }
    if let Some(y1) = data.one_year_return {
        parts.push(format!("1-year: {:.1}%", y1));
    }
    if let Some(y3) = data.three_year_return {
        parts.push(format!("3-year: {:.1}%", y3));
    }
    if parts.is_empty() {
        "Performance data unavailable.".to_string()
    } else {
        parts.join(", ") + "."
    }
}

pub fn expense_check(data: &EtfData) -> String {
    match data.expense_ratio {
        Some(er) => {
            let pct = er * 100.0;
            let context = if pct < 0.10 {
                "well below the industry average — very cost-efficient"
            } else if pct < 0.30 {
                "below the industry average — cost-efficient"
            } else if pct < 0.60 {
                "near the industry average"
            } else {
                "above average — this fee drag compounds significantly over time"
            };
            format!("Annual expense ratio: {:.2}% — {}", pct, context)
        }
        None => "Expense ratio data unavailable.".to_string(),
    }
}

pub fn change_mind(_data: &EtfData, decision: &Decision) -> String {
    match decision {
        Decision::Buy => {
            "A BUY opinion would be reconsidered if the expense ratio increases, \
            AUM drops significantly, or the fund strategy shifts to a more concentrated/leveraged approach.".to_string()
        }
        Decision::Pass | Decision::Sell => {
            "A more favorable opinion would require a lower expense ratio, \
            broader diversification, or improved trailing returns relative to category peers.".to_string()
        }
    }
}

pub fn study_next(_data: &EtfData, scores: &[CategoryScore], name: &str) -> String {
    let weakest = scores.iter().min_by(|a, b| a.score.partial_cmp(&b.score).unwrap());

    match weakest.map(|s| s.name) {
        Some("Expense Ratio") => format!("{name} should study how expense ratios compound over time and why even small differences matter significantly over a 20-year horizon."),
        Some("Diversification") => format!("{name} should explore the difference between concentrated sector ETFs and broad market index funds."),
        Some("AUM / Liquidity") => format!("{name} should learn about ETF liquidity risk and why fund size matters for buying and selling shares efficiently."),
        Some("Performance") => format!("{name} should study how to compare ETF returns against a benchmark index and what tracking error means."),
        _ => format!("{name} should explore how to compare ETFs across the same category using expense ratios, AUM, and trailing returns."),
    }
}

pub fn translation(data: &EtfData, decision: &Decision, name: &str) -> String {
    let fund = data.fund_name.as_deref().unwrap_or(&data.symbol);
    match decision {
        Decision::Buy => format!(
            "{name}, in plain English: {fund} appears to be a solid, cost-effective choice. \
            The expense ratio is reasonable and the fund offers good diversification."
        ),
        Decision::Pass => format!(
            "{name}, in plain English: {fund} is neither clearly great nor clearly bad. \
            There are some concerns worth noting, but nothing that makes it an obvious avoid."
        ),
        Decision::Sell => format!(
            "{name}, in plain English: {fund} has some meaningful drawbacks — \
            likely a high expense ratio, concentrated holdings, or poor recent performance."
        ),
    }
}

pub fn final_reason(data: &EtfData, decision: &Decision, scores: &[CategoryScore]) -> String {
    if data.is_leveraged || data.is_inverse {
        return "Leveraged/inverse ETF structure makes it unsuitable for long-term investors".to_string();
    }

    let top = scores.iter().max_by(|a, b| {
        (a.score * a.weight).abs().partial_cmp(&(b.score * b.weight).abs()).unwrap()
    });

    match top {
        Some(s) => s.detail.clone(),
        None => format!("{} based on overall fund characteristics", decision.label()),
    }
}