//! # Real Data Loader - DBN to ML Features
//!
//! Loads real market data from Databento DBN files and converts to ML-ready features.
//! This module bridges the gap between raw market data and ML model input, providing
//! feature extraction, technical indicators, and data quality validation.
//!
//! ## Architecture
//!
//! ```text
//! ┌──────────────┐     ┌──────────────┐     ┌──────────────┐
//! │  DBN Files   │────▶│  Data Loader │────▶│  ML Features │
//! │  (Parquet)   │     │  + Indicators│     │  (Normalized)│
//! └──────────────┘     └──────────────┘     └──────────────┘
//!        │                     │                     │
//!        ▼                     ▼                     ▼
//!   OHLCV Bars          Technical Indicators    Feature Matrix
//!   Timestamps          RSI, MACD, BB, etc.     Ready for ML
//! ```
//!
//! ## Usage
//!
//! ```rust
//! use ml::data_loader::RealDataLoader;
//!
//! let loader = RealDataLoader::new("test_data/real/databento").await?;
//! let bars = loader.load_symbol_data("ZN.FUT").await?;
//! let features = loader.extract_features(&bars)?;
//! let indicators = loader.calculate_indicators(&bars)?;
//! ```

use anyhow::{Context, Result};
use chrono::DateTime;
use dbn::decode::{DbnDecoder, DecodeRecordRef};
use dbn::OhlcvMsg;
use std::collections::HashMap;
use std::path::{Path, PathBuf};
use tracing::{debug, info};

use crate::types::OHLCVBar;

/// Feature matrix for ML model input
///
/// Contains normalized features ready for ML training/inference:
/// - prices: OHLCV data (normalized)
/// - returns: Log returns
/// - volume: Normalized volume
/// - indicators: Technical indicators (10 essential ones)
#[derive(Debug, Clone)]
pub struct FeatureMatrix {
    /// OHLCV prices (each bar is [open, high, low, close, volume])
    pub prices: Vec<Vec<f32>>,
    /// Log returns (close-to-close)
    pub returns: Vec<f32>,
    /// Normalized volume
    pub volume: Vec<f32>,
    /// Technical indicators
    pub indicators: Vec<Vec<f32>>,
}

/// Technical indicators (10 essential ones)
///
/// All indicators are calculated with standard parameters for 1-minute OHLCV data:
/// - RSI(14): Relative Strength Index
/// - MACD(12,26,9): Moving Average Convergence Divergence
/// - Bollinger Bands(20, 2.0): Price envelope
/// - ATR(14): Average True Range
/// - EMA(12, 26): Exponential moving averages
/// - Volume MA(20): Volume moving average
#[derive(Debug, Clone)]
pub struct Indicators {
    /// RSI(14) - values 0-100
    pub rsi: Vec<f32>,
    /// MACD line (12,26)
    pub macd: Vec<f32>,
    /// MACD signal line (9)
    pub macd_signal: Vec<f32>,
    /// Bollinger upper band (20, 2.0)
    pub bb_upper: Vec<f32>,
    /// Bollinger middle band (SMA 20)
    pub bb_middle: Vec<f32>,
    /// Bollinger lower band (20, 2.0)
    pub bb_lower: Vec<f32>,
    /// ATR(14) - volatility measure
    pub atr: Vec<f32>,
    /// EMA(12) - fast exponential moving average
    pub ema_fast: Vec<f32>,
    /// EMA(26) - slow exponential moving average
    pub ema_slow: Vec<f32>,
    /// Volume MA(20) - volume moving average
    pub volume_ma: Vec<f32>,
}

/// Real data loader for DBN files
///
/// Loads OHLCV data from Databento DBN files and extracts ML-ready features.
#[derive(Debug)]
pub struct RealDataLoader {
    /// Base directory containing DBN files
    base_path: PathBuf,
    /// Cached symbol data
    cache: HashMap<String, Vec<OHLCVBar>>,
}

impl RealDataLoader {
    /// Create new data loader with automatic workspace root detection
    ///
    /// Checks `FOXHUNT_DATA_DIR` env var first (used in CI with PVC mount),
    /// then falls back to finding workspace root by looking for Cargo.toml.
    pub fn new_from_workspace() -> Result<Self> {
        // CI / PVC override: FOXHUNT_DATA_DIR points directly at the data directory
        if let Ok(data_dir) = std::env::var("FOXHUNT_DATA_DIR") {
            let base_path = PathBuf::from(&data_dir);
            if base_path.exists() {
                info!("Using FOXHUNT_DATA_DIR: {}", data_dir);
                return Ok(Self {
                    base_path,
                    cache: HashMap::new(),
                });
            }
        }

        let mut current = std::env::current_dir()?;

        // Try to find workspace root
        while !current.join("Cargo.toml").exists() || !current.join("test_data").exists() {
            if !current.pop() {
                return Err(anyhow::anyhow!("Could not find workspace root"));
            }
        }

        let base_path = current.join("test_data/real/databento");
        Ok(Self {
            base_path,
            cache: HashMap::new(),
        })
    }

    /// Create new data loader
    ///
    /// # Arguments
    ///
    /// * `base_path` - Directory containing DBN files (e.g., "test_data/real/databento")
    pub fn new<P: AsRef<Path>>(base_path: P) -> Self {
        Self {
            base_path: base_path.as_ref().to_path_buf(),
            cache: HashMap::new(),
        }
    }

    /// Load OHLCV data from DBN file
    ///
    /// Searches for DBN files matching the symbol pattern and loads OHLCV bars.
    /// Caches loaded data for repeated access.
    ///
    /// # Arguments
    ///
    /// * `symbol` - Symbol to load (e.g., "ZN.FUT", "6E.FUT", "ES.FUT")
    ///
    /// # Returns
    ///
    /// Vector of OHLCV bars sorted by timestamp
    pub async fn load_symbol_data(&mut self, symbol: &str) -> Result<Vec<OHLCVBar>> {
        // Check cache first
        if let Some(cached) = self.cache.get(symbol) {
            debug!(
                "Returning cached data for {}: {} bars",
                symbol,
                cached.len()
            );
            return Ok(cached.clone());
        }

        info!("Loading DBN data for symbol: {}", symbol);

        // Find DBN file for this symbol
        let dbn_file = self.find_dbn_file(symbol)?;
        info!("Found DBN file: {:?}", dbn_file);

        // Load and parse DBN file
        let bars = self.parse_dbn_file(&dbn_file)?;
        info!("Loaded {} bars for {}", bars.len(), symbol);

        // Cache the data
        self.cache.insert(symbol.to_string(), bars.clone());

        Ok(bars)
    }

    /// Find DBN file for symbol
    ///
    /// Searches base_path and per-symbol subdirectories for files matching
    /// `{symbol}*.dbn` or `{symbol}*.dbn.zst`. Preference order:
    /// 1. `*.uncompressed.dbn` (pre-decompressed)
    /// 2. `*.dbn` (raw uncompressed)
    /// 3. `*.dbn.zst` (zstd-compressed, standard Databento download format)
    fn find_dbn_file(&self, symbol: &str) -> Result<PathBuf> {
        // Search base_path and also the per-symbol subdirectory (Databento layout)
        let mut search_dirs = vec![self.base_path.clone()];
        let subdir = self.base_path.join(symbol);
        if subdir.is_dir() {
            search_dirs.push(subdir);
        }

        let mut uncompressed = Vec::new();
        let mut compressed = Vec::new();

        for search_dir in &search_dirs {
            let dir = match std::fs::read_dir(search_dir) {
                Ok(d) => d,
                Err(_) => continue,
            };

            for entry in dir {
                let entry = entry?;
                let path = entry.path();
                let filename = path.file_name().and_then(|s| s.to_str()).unwrap_or("");

                if !filename.starts_with(symbol) {
                    continue;
                }

                // Prefer uncompressed files (dbn 0.42.0 compatibility)
                if filename.contains(".uncompressed.dbn") {
                    return Ok(path);
                }
                if filename.ends_with(".dbn.zst") {
                    compressed.push(path);
                } else if filename.ends_with(".dbn") {
                    uncompressed.push(path);
                } else {
                    // Skip non-DBN files
                }
            }
        }

        // Prefer raw .dbn over .dbn.zst
        if let Some(path) = uncompressed.into_iter().next() {
            return Ok(path);
        }
        compressed
            .into_iter()
            .next()
            .ok_or_else(|| anyhow::anyhow!("No DBN file found for symbol: {}", symbol))
    }

    /// Parse DBN file and extract OHLCV bars
    ///
    /// Uses the `dbn` crate to decode DBN binary format and extract OHLCV records.
    /// Handles both raw `.dbn` and zstd-compressed `.dbn.zst` files.
    fn parse_dbn_file(&self, path: &Path) -> Result<Vec<OHLCVBar>> {
        let filename = path.file_name().and_then(|s| s.to_str()).unwrap_or("");

        // Use zstd decoder for compressed files, raw decoder otherwise
        let bars = if filename.ends_with(".dbn.zst") {
            let mut decoder = DbnDecoder::from_zstd_file(path)
                .context(format!("Failed to open zstd DBN file: {:?}", path))?;
            Self::decode_ohlcv_bars(&mut decoder)?
        } else {
            let mut decoder = DbnDecoder::from_file(path)
                .context(format!("Failed to open DBN file: {:?}", path))?;
            Self::decode_ohlcv_bars(&mut decoder)?
        };

        Ok(bars)
    }

    /// Decode OHLCV bars from a DBN decoder (generic over reader type)
    fn decode_ohlcv_bars<R: std::io::Read>(decoder: &mut DbnDecoder<R>) -> Result<Vec<OHLCVBar>> {
        let mut bars = Vec::new();

        // Iterate over records
        while let Some(record_ref) = decoder
            .decode_record_ref()
            .context("Failed to decode record")?
        {
            if let Some(ohlcv) = record_ref.get::<OhlcvMsg>() {
                // Convert DBN record to OHLCVBar
                let bar = OHLCVBar {
                    timestamp: DateTime::from_timestamp_nanos(ohlcv.hd.ts_event as i64),
                    open: ohlcv.open as f64 / 1e9, // DBN stores prices in fixed-point
                    high: ohlcv.high as f64 / 1e9,
                    low: ohlcv.low as f64 / 1e9,
                    close: ohlcv.close as f64 / 1e9,
                    volume: ohlcv.volume as f64,
                };

                bars.push(bar);
            }
        }

        // Sort by timestamp (should already be sorted but ensure it)
        bars.sort_by_key(|b| b.timestamp);

        Ok(bars)
    }

    /// Extract basic features for ML models
    ///
    /// Converts OHLCV bars to normalized feature matrix:
    /// - prices: OHLCV data (normalized to 0-1 range per feature)
    /// - returns: Log returns (close-to-close)
    /// - volume: Normalized volume
    ///
    /// # Arguments
    ///
    /// * `bars` - OHLCV bars to extract features from
    pub fn extract_features(&self, bars: &[OHLCVBar]) -> Result<FeatureMatrix> {
        if bars.is_empty() {
            return Err(anyhow::anyhow!(
                "Cannot extract features from empty bar sequence"
            ));
        }

        let mut prices = Vec::with_capacity(bars.len());
        let mut returns = Vec::with_capacity(bars.len());
        let mut volume = Vec::with_capacity(bars.len());

        // Calculate normalization factors
        let (price_min, price_max) = self.price_range(bars);
        let (vol_min, vol_max) = self.volume_range(bars);

        // Extract features
        for (i, bar) in bars.iter().enumerate() {
            // Normalize OHLCV to 0-1 range
            let norm_open = ((bar.open - price_min) / (price_max - price_min)) as f32;
            let norm_high = ((bar.high - price_min) / (price_max - price_min)) as f32;
            let norm_low = ((bar.low - price_min) / (price_max - price_min)) as f32;
            let norm_close = ((bar.close - price_min) / (price_max - price_min)) as f32;
            let norm_volume = ((bar.volume - vol_min) / (vol_max - vol_min)) as f32;

            prices.push(vec![
                norm_open,
                norm_high,
                norm_low,
                norm_close,
                norm_volume,
            ]);
            volume.push(norm_volume);

            // Calculate log returns (skip first bar)
            if i > 0 {
                let log_return = ((bar.close / bars[i - 1].close).ln()) as f32;
                returns.push(log_return);
            }
        }

        // First return is 0 (no previous bar)
        if !returns.is_empty() {
            returns.insert(0, 0.0);
        }

        Ok(FeatureMatrix {
            prices,
            returns,
            volume,
            indicators: Vec::new(), // Filled by calculate_indicators()
        })
    }

    /// Calculate technical indicators
    ///
    /// Computes 10 essential technical indicators:
    /// - RSI(14): Relative Strength Index
    /// - MACD(12,26,9): Moving Average Convergence Divergence
    /// - Bollinger Bands(20, 2.0): Price envelope
    /// - ATR(14): Average True Range
    /// - EMA(12, 26): Exponential moving averages
    /// - Volume MA(20): Volume moving average
    ///
    /// # Arguments
    ///
    /// * `bars` - OHLCV bars to calculate indicators from
    pub fn calculate_indicators(&self, bars: &[OHLCVBar]) -> Result<Indicators> {
        if bars.len() < 26 {
            return Err(anyhow::anyhow!(
                "Need at least 26 bars to calculate indicators (got {})",
                bars.len()
            ));
        }

        let closes: Vec<f64> = bars.iter().map(|b| b.close).collect();
        let volumes: Vec<f64> = bars.iter().map(|b| b.volume).collect();

        Ok(Indicators {
            rsi: self.calculate_rsi(&closes, 14)?,
            macd: self.calculate_macd(&closes, 12, 26)?,
            macd_signal: self.calculate_macd_signal(&closes, 12, 26, 9)?,
            bb_upper: self.calculate_bb_upper(&closes, 20, 2.0)?,
            bb_middle: self.calculate_sma(&closes, 20)?,
            bb_lower: self.calculate_bb_lower(&closes, 20, 2.0)?,
            atr: self.calculate_atr(bars, 14)?,
            ema_fast: self.calculate_ema(&closes, 12)?,
            ema_slow: self.calculate_ema(&closes, 26)?,
            volume_ma: self.calculate_sma(&volumes, 20)?,
        })
    }

    // ===== Technical Indicator Calculations =====

    /// Calculate RSI (Relative Strength Index)
    fn calculate_rsi(&self, prices: &[f64], period: usize) -> Result<Vec<f32>> {
        let mut rsi = Vec::with_capacity(prices.len());

        for i in 0..prices.len() {
            if i < period {
                rsi.push(50.0); // Neutral RSI for warmup period
                continue;
            }

            let mut gains = 0.0;
            let mut losses = 0.0;

            for j in (i - period + 1)..=i {
                let change = prices[j] - prices[j - 1];
                if change > 0.0 {
                    gains += change;
                } else {
                    losses += -change;
                }
            }

            let avg_gain = gains / period as f64;
            let avg_loss = losses / period as f64;

            let rs = if avg_loss > 0.0 {
                avg_gain / avg_loss
            } else {
                100.0 // Max RSI when no losses
            };

            let rsi_value = 100.0 - (100.0 / (1.0 + rs));
            rsi.push(rsi_value as f32);
        }

        Ok(rsi)
    }

    /// Calculate MACD line
    fn calculate_macd(&self, prices: &[f64], fast: usize, slow: usize) -> Result<Vec<f32>> {
        let ema_fast = self.calculate_ema(prices, fast)?;
        let ema_slow = self.calculate_ema(prices, slow)?;

        Ok(ema_fast
            .iter()
            .zip(ema_slow.iter())
            .map(|(f, s)| f - s)
            .collect())
    }

    /// Calculate MACD signal line
    fn calculate_macd_signal(
        &self,
        prices: &[f64],
        fast: usize,
        slow: usize,
        signal: usize,
    ) -> Result<Vec<f32>> {
        let macd = self.calculate_macd(prices, fast, slow)?;
        let macd_f64: Vec<f64> = macd.iter().map(|&x| x as f64).collect();
        self.calculate_ema(&macd_f64, signal)
    }

    /// Calculate Bollinger upper band
    fn calculate_bb_upper(&self, prices: &[f64], period: usize, num_std: f64) -> Result<Vec<f32>> {
        let sma = self.calculate_sma(prices, period)?;
        let mut upper = Vec::with_capacity(prices.len());

        for i in 0..prices.len() {
            if i < period - 1 {
                upper.push(prices[i] as f32);
                continue;
            }

            let window = &prices[i - period + 1..=i];
            let mean = window.iter().sum::<f64>() / period as f64;
            let variance = window.iter().map(|&x| (x - mean).powi(2)).sum::<f64>() / period as f64;
            let std_dev = variance.sqrt();

            upper.push((sma[i] as f64 + num_std * std_dev) as f32);
        }

        Ok(upper)
    }

    /// Calculate Bollinger lower band
    fn calculate_bb_lower(&self, prices: &[f64], period: usize, num_std: f64) -> Result<Vec<f32>> {
        let sma = self.calculate_sma(prices, period)?;
        let mut lower = Vec::with_capacity(prices.len());

        for i in 0..prices.len() {
            if i < period - 1 {
                lower.push(prices[i] as f32);
                continue;
            }

            let window = &prices[i - period + 1..=i];
            let mean = window.iter().sum::<f64>() / period as f64;
            let variance = window.iter().map(|&x| (x - mean).powi(2)).sum::<f64>() / period as f64;
            let std_dev = variance.sqrt();

            lower.push((sma[i] as f64 - num_std * std_dev) as f32);
        }

        Ok(lower)
    }

    /// Calculate ATR (Average True Range)
    fn calculate_atr(&self, bars: &[OHLCVBar], period: usize) -> Result<Vec<f32>> {
        let mut atr = Vec::with_capacity(bars.len());

        for i in 0..bars.len() {
            if i < period {
                atr.push(0.0);
                continue;
            }

            let mut true_ranges = Vec::with_capacity(period);
            for j in (i - period + 1)..=i {
                let high_low = bars[j].high - bars[j].low;
                let high_close = if j > 0 {
                    (bars[j].high - bars[j - 1].close).abs()
                } else {
                    high_low
                };
                let low_close = if j > 0 {
                    (bars[j].low - bars[j - 1].close).abs()
                } else {
                    high_low
                };

                let true_range = high_low.max(high_close).max(low_close);
                true_ranges.push(true_range);
            }

            let avg_tr = true_ranges.iter().sum::<f64>() / period as f64;
            atr.push(avg_tr as f32);
        }

        Ok(atr)
    }

    /// Calculate EMA (Exponential Moving Average)
    fn calculate_ema(&self, prices: &[f64], period: usize) -> Result<Vec<f32>> {
        if prices.len() < period {
            return Err(anyhow::anyhow!(
                "Need at least {} prices for EMA (got {})",
                period,
                prices.len()
            ));
        }

        let mut ema = Vec::with_capacity(prices.len());
        let alpha = 2.0 / (period as f64 + 1.0);

        // Start with SMA for first period
        let initial_sma = prices[..period].iter().sum::<f64>() / period as f64;
        ema.extend(vec![initial_sma as f32; period]);

        // Calculate EMA for remaining values
        for i in period..prices.len() {
            let prev_ema = ema[i - 1] as f64;
            let new_ema = alpha * prices[i] + (1.0 - alpha) * prev_ema;
            ema.push(new_ema as f32);
        }

        Ok(ema)
    }

    /// Calculate SMA (Simple Moving Average)
    fn calculate_sma(&self, prices: &[f64], period: usize) -> Result<Vec<f32>> {
        let mut sma = Vec::with_capacity(prices.len());

        for i in 0..prices.len() {
            if i < period - 1 {
                sma.push(prices[i] as f32);
                continue;
            }

            let window = &prices[i - period + 1..=i];
            let avg = window.iter().sum::<f64>() / period as f64;
            sma.push(avg as f32);
        }

        Ok(sma)
    }

    // ===== Helper Methods =====

    fn price_range(&self, bars: &[OHLCVBar]) -> (f64, f64) {
        let mut min = f64::MAX;
        let mut max = f64::MIN;

        for bar in bars {
            min = min.min(bar.low);
            max = max.max(bar.high);
        }

        (min, max)
    }

    fn volume_range(&self, bars: &[OHLCVBar]) -> (f64, f64) {
        let mut min = f64::MAX;
        let mut max = f64::MIN;

        for bar in bars {
            min = min.min(bar.volume);
            max = max.max(bar.volume);
        }

        (min, max)
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[tokio::test]
    #[ignore] // Requires local DBN data files
    async fn test_load_symbol_data() -> Result<()> {
        let mut loader = RealDataLoader::new_from_workspace()?;

        // Test loading ZN.FUT (should have ~29K bars)
        let bars = loader.load_symbol_data("ZN.FUT").await?;
        assert!(bars.len() > 1000, "Expected >1000 bars, got {}", bars.len());

        // Validate bar integrity
        for bar in bars.iter().take(100) {
            assert!(bar.high >= bar.low, "High < Low: {:?}", bar);
            assert!(bar.high >= bar.open, "High < Open: {:?}", bar);
            assert!(bar.high >= bar.close, "High < Close: {:?}", bar);
            assert!(bar.low <= bar.open, "Low > Open: {:?}", bar);
            assert!(bar.low <= bar.close, "Low > Close: {:?}", bar);
            assert!(bar.volume >= 0.0, "Negative volume: {:?}", bar);
        }

        println!("✅ Loaded {} bars for ZN.FUT", bars.len());
        Ok(())
    }

    #[tokio::test]
    #[ignore] // Requires local DBN data files
    async fn test_extract_features() -> Result<()> {
        let mut loader = RealDataLoader::new_from_workspace()?;
        let bars = loader.load_symbol_data("ZN.FUT").await?;

        let features = loader.extract_features(&bars)?;

        assert_eq!(features.prices.len(), bars.len());
        assert_eq!(features.returns.len(), bars.len());
        assert_eq!(features.volume.len(), bars.len());

        // Check normalization (should be 0-1 range)
        for price_vec in features.prices.iter().take(100) {
            for &val in price_vec {
                assert!(val >= 0.0 && val <= 1.0, "Price not normalized: {}", val);
            }
        }

        println!(
            "✅ Feature extraction working: {} bars, 5 features/bar",
            bars.len()
        );
        Ok(())
    }

    #[tokio::test]
    #[ignore] // Requires local DBN data files
    async fn test_calculate_indicators() -> Result<()> {
        let mut loader = RealDataLoader::new_from_workspace()?;
        let bars = loader.load_symbol_data("ZN.FUT").await?;

        let indicators = loader.calculate_indicators(&bars)?;

        assert_eq!(indicators.rsi.len(), bars.len());
        assert_eq!(indicators.macd.len(), bars.len());
        assert_eq!(indicators.ema_fast.len(), bars.len());

        // Check RSI validity (0-100 range)
        for &rsi in indicators.rsi.iter().skip(14).take(100) {
            assert!(rsi >= 0.0 && rsi <= 100.0, "Invalid RSI: {}", rsi);
        }

        // Check ATR validity (non-negative)
        for &atr in indicators.atr.iter().skip(14).take(100) {
            assert!(atr >= 0.0, "Invalid ATR: {}", atr);
        }

        println!(
            "✅ Indicators calculated: 10 indicators × {} bars",
            bars.len()
        );
        Ok(())
    }
}