foxhunt/ml/examples/wave_d_backtest.rs

//! Wave D Comparison Backtest (225 Features with Regime Detection)
//!
//! This backtest evaluates Wave D performance (225 features with regime detection)
//! to compare against Wave C baseline (201 features, no regime detection).
//!
//! Usage:
//!   cargo run -p ml --example wave_d_backtest --release

use anyhow::Result;
use candle_core::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use chrono::{DateTime, Utc};
use common::ml_strategy::MLFeatureExtractor;
use data::providers::databento::dbn_parser::{DbnParser, ProcessedMessage};
use ml::dqn::dqn::Sequential;
use num_traits::ToPrimitive;
use std::path::PathBuf;

/// Performance metrics for backtest
#[derive(Debug, Clone)]
struct PerformanceMetrics {
    total_trades: usize,
    winning_trades: usize,
    win_rate: f64,
    total_pnl: f64,
    total_return: f64,
    sharpe_ratio: f64,
    max_drawdown: f64,
    calmar_ratio: f64,
    profit_factor: f64,
}

/// Trade record
#[derive(Debug, Clone)]
struct Trade {
    entry_time: DateTime<Utc>,
    exit_time: DateTime<Utc>,
    entry_price: f64,
    exit_price: f64,
    side: TradeSide,
    pnl: f64,
    size: f64,
}

#[derive(Debug, Clone, Copy)]
enum TradeSide {
    Long,
    Short,
}

/// Market data bar
#[derive(Debug, Clone)]
struct MarketBar {
    timestamp: DateTime<Utc>,
    open: f64,
    high: f64,
    low: f64,
    close: f64,
    volume: f64,
}

/// DQN model wrapper
struct DQNModel {
    network: Sequential,
    device: Device,
}

impl DQNModel {
    /// Load DQN model from SafeTensors
    fn load(model_path: PathBuf) -> Result<Self> {
        let device = Device::cuda_if_available(0)?;
        println!("🔧 Loading DQN model on device: {:?}", device);

        // Load SafeTensors checkpoint
        let _vb = unsafe {
            VarBuilder::from_mmaped_safetensors(&[model_path.clone()], DType::F32, &device)?
        };

        // Create DQN network (Wave D: 225 features * 4 = 900 input)
        let dqn_network = Sequential::new(
            900,            // state_dim (225 features * 4 lookback)
            &[128, 64, 32], // hidden_dims
            3,              // num_actions (Buy, Sell, Hold)
            device.clone(),
        )
        .map_err(|e| anyhow::anyhow!("Failed to create DQN network: {}", e))?;

        println!("✅ DQN model loaded successfully");

        Ok(Self {
            network: dqn_network,
            device,
        })
    }

    /// Predict trading signal from features
    /// Returns: (signal_strength: -1.0 to 1.0, confidence: 0.0 to 1.0)
    fn predict(&self, features: &[f64]) -> Result<(f64, f64)> {
        // Pad or truncate features to 900 dimensions (225 * 4)
        let mut padded_features = features.to_vec();
        while padded_features.len() < 900 {
            padded_features.push(0.0);
        }
        if padded_features.len() > 900 {
            padded_features.truncate(900);
        }

        // Convert to f32 for candle tensors
        let features_f32: Vec<f32> = padded_features.iter().map(|&x| x as f32).collect();

        // Create tensor [1, 900]
        let feature_tensor = Tensor::from_vec(features_f32, (1, 900), &self.device)?;

        // Run inference
        let q_values = self
            .network
            .forward(&feature_tensor)
            .map_err(|e| anyhow::anyhow!("DQN forward pass failed: {}", e))?;

        // Get action probabilities
        let q_vec = q_values.to_vec2::<f32>()?;
        let actions = &q_vec[0]; // [Buy, Sell, Hold]

        // Convert action values to signal (-1 to 1)
        let buy_strength = actions[0] as f64;
        let sell_strength = actions[1] as f64;
        let hold_strength = actions[2] as f64;

        // Normalize to -1 to 1 range
        let signal = if buy_strength > sell_strength && buy_strength > hold_strength {
            (buy_strength - hold_strength).min(1.0)
        } else if sell_strength > buy_strength && sell_strength > hold_strength {
            -(sell_strength - hold_strength).min(1.0)
        } else {
            0.0
        };

        // Confidence based on action strength difference
        let max_action = buy_strength.max(sell_strength).max(hold_strength);
        let confidence = (max_action - hold_strength).abs().min(1.0);

        Ok((signal, confidence.max(0.5)))
    }
}

/// Load market data from DBN file
fn load_market_data(dbn_path: &PathBuf) -> Result<Vec<MarketBar>> {
    println!("📖 Loading market data from: {}", dbn_path.display());

    // Create parser
    let parser =
        DbnParser::new().map_err(|e| anyhow::anyhow!("Failed to create DBN parser: {}", e))?;

    // Read DBN file
    let dbn_bytes = std::fs::read(dbn_path)?;

    // Parse batch
    let messages = parser
        .parse_batch(&dbn_bytes)
        .map_err(|e| anyhow::anyhow!("Failed to parse DBN file: {}", e))?;

    let mut bars = Vec::new();

    for msg in messages {
        if let ProcessedMessage::Ohlcv {
            symbol: _,
            timestamp,
            open,
            high,
            low,
            close,
            volume,
        } = msg
        {
            // Convert to f64
            let ts_secs = (timestamp.as_nanos() / 1_000_000_000) as i64;
            bars.push(MarketBar {
                timestamp: DateTime::from_timestamp(ts_secs, 0).unwrap_or_else(|| Utc::now()),
                open: open.to_f64(),
                high: high.to_f64(),
                low: low.to_f64(),
                close: close.to_f64(),
                volume: volume.to_f64().unwrap_or(0.0),
            });
        }
    }

    // Sort by timestamp
    bars.sort_by_key(|bar| bar.timestamp);

    println!("✅ Loaded {} bars", bars.len());

    Ok(bars)
}

/// Calculate maximum drawdown from equity curve
fn calculate_max_drawdown(equity_curve: &[f64]) -> f64 {
    let mut max_drawdown = 0.0;
    let mut peak = equity_curve[0];

    for &equity in equity_curve {
        if equity > peak {
            peak = equity;
        }
        let drawdown = (peak - equity) / peak;
        if drawdown > max_drawdown {
            max_drawdown = drawdown;
        }
    }

    max_drawdown
}

/// Run Wave D backtest (225 features with regime detection)
fn run_backtest(
    model: &DQNModel,
    market_data: &[MarketBar],
    initial_capital: f64,
) -> Result<PerformanceMetrics> {
    println!("\n🔄 Running Wave D backtest (225 features with regime detection)...");

    // Initialize Wave D feature extractor (225 features)
    let mut feature_extractor = MLFeatureExtractor::new_wave_d(20);

    let mut trades = Vec::new();
    let mut position: Option<(TradeSide, f64, DateTime<Utc>, f64)> = None; // (side, size, entry_time, entry_price)
    let mut equity_curve = vec![initial_capital];
    let mut current_capital = initial_capital;

    // Feature history buffer (225 features * 4 lookback = 900)
    let mut feature_history: Vec<Vec<f64>> = Vec::new();

    for i in 0..market_data.len() {
        let bar = &market_data[i];

        // Extract Wave D features (225 features)
        let current_features = feature_extractor.extract_features(bar.close, bar.volume, bar.timestamp);

        feature_history.push(current_features);

        // Keep only last 4 periods (lookback)
        if feature_history.len() > 4 {
            feature_history.remove(0);
        }

        // Skip if insufficient lookback
        if feature_history.len() < 4 {
            continue;
        }

        // Flatten features: [225 * 4 = 900]
        let flat_features: Vec<f64> = feature_history.iter().flatten().copied().collect();

        // Get model prediction
        let (signal, confidence) = model.predict(&flat_features)?;

        // Trading logic
        let signal_threshold = 0.3;
        let confidence_threshold = 0.7;

        if position.is_none() && signal.abs() > signal_threshold && confidence > confidence_threshold
        {
            // Enter position
            let side = if signal > 0.0 {
                TradeSide::Long
            } else {
                TradeSide::Short
            };
            let size = (current_capital * 0.1) / bar.close; // 10% of capital
            position = Some((side, size, bar.timestamp, bar.close));
        } else if let Some((side, size, entry_time, entry_price)) = position {
            // Exit logic: signal reversal or 10-bar holding period
            let should_exit = match side {
                TradeSide::Long => signal < -0.2 || (i as i64 - entry_time.timestamp()) > 600,
                TradeSide::Short => signal > 0.2 || (i as i64 - entry_time.timestamp()) > 600,
            };

            if should_exit {
                let pnl = match side {
                    TradeSide::Long => size * (bar.close - entry_price),
                    TradeSide::Short => size * (entry_price - bar.close),
                };

                current_capital += pnl;
                equity_curve.push(current_capital);

                trades.push(Trade {
                    entry_time,
                    exit_time: bar.timestamp,
                    entry_price,
                    exit_price: bar.close,
                    side,
                    pnl,
                    size,
                });

                position = None;
            }
        }
    }

    // Close any open position at the end
    if let Some((side, size, entry_time, entry_price)) = position {
        let last_bar = &market_data[market_data.len() - 1];
        let pnl = match side {
            TradeSide::Long => size * (last_bar.close - entry_price),
            TradeSide::Short => size * (entry_price - last_bar.close),
        };
        current_capital += pnl;
        equity_curve.push(current_capital);

        trades.push(Trade {
            entry_time,
            exit_time: last_bar.timestamp,
            entry_price,
            exit_price: last_bar.close,
            side,
            pnl,
            size,
        });
    }

    // Calculate metrics
    let total_trades = trades.len();
    let winning_trades = trades.iter().filter(|t| t.pnl > 0.0).count();
    let win_rate = if total_trades > 0 {
        (winning_trades as f64 / total_trades as f64) * 100.0
    } else {
        0.0
    };
    let total_pnl: f64 = trades.iter().map(|t| t.pnl).sum();
    let total_return = (current_capital - initial_capital) / initial_capital * 100.0;

    // Sharpe ratio (annualized)
    let returns: Vec<f64> = trades
        .iter()
        .map(|t| t.pnl / initial_capital)
        .collect();
    let sharpe_ratio = if !returns.is_empty() {
        let mean_return = returns.iter().sum::<f64>() / returns.len() as f64;
        let variance = returns
            .iter()
            .map(|r| (r - mean_return).powi(2))
            .sum::<f64>()
            / returns.len() as f64;
        let std_dev = variance.sqrt();
        if std_dev > 0.0 {
            (mean_return / std_dev) * (252.0_f64).sqrt()
        } else {
            0.0
        }
    } else {
        0.0
    };

    // Max drawdown
    let max_drawdown = calculate_max_drawdown(&equity_curve) * 100.0;

    // Calmar ratio
    let calmar_ratio = if max_drawdown > 0.0 {
        total_return / max_drawdown
    } else {
        0.0
    };

    // Profit factor
    let gross_profit: f64 = trades.iter().filter(|t| t.pnl > 0.0).map(|t| t.pnl).sum();
    let gross_loss: f64 = trades
        .iter()
        .filter(|t| t.pnl < 0.0)
        .map(|t| t.pnl.abs())
        .sum();
    let profit_factor = if gross_loss > 0.0 {
        gross_profit / gross_loss
    } else if gross_profit > 0.0 {
        f64::INFINITY
    } else {
        0.0
    };

    Ok(PerformanceMetrics {
        total_trades,
        winning_trades,
        win_rate,
        total_pnl,
        total_return,
        sharpe_ratio,
        max_drawdown,
        calmar_ratio,
        profit_factor,
    })
}

fn main() -> Result<()> {
    println!("\n{}", "=".repeat(70));
    println!("🚀 WAVE D COMPARISON BACKTEST (225 Features + Regime Detection)");
    println!("{}\n", "=".repeat(70));

    // Configuration
    let model_path = PathBuf::from("/home/jgrusewski/Work/foxhunt/ml/trained_models/dqn_final_epoch100.safetensors");
    let data_path = PathBuf::from("/home/jgrusewski/Work/foxhunt/test_data/real/databento/ES.FUT_ohlcv-1m_2024-01-02.dbn");
    let initial_capital = 100000.0;

    // Load model
    let model = DQNModel::load(model_path)?;

    // Load market data
    let market_data = load_market_data(&data_path)?;

    println!("📊 Backtest Configuration:");
    println!("   Symbol:           ES.FUT");
    println!("   Bars:             {}", market_data.len());
    println!("   Initial Capital:  ${:.2}", initial_capital);
    println!("   Feature Set:      Wave D (225 features)");
    println!("   Regime Detection: ON");

    // Run backtest
    let metrics = run_backtest(&model, &market_data, initial_capital)?;

    // Print results
    println!("\n{}", "=".repeat(70));
    println!("📈 WAVE D BACKTEST RESULTS");
    println!("{}", "=".repeat(70));
    println!("\n💰 Performance Metrics:");
    println!("   Total Trades:      {}", metrics.total_trades);
    println!("   Winning Trades:    {}", metrics.winning_trades);
    println!("   Win Rate:          {:.2}%", metrics.win_rate);
    println!("   Total PnL:         ${:.2}", metrics.total_pnl);
    println!("   Total Return:      {:.2}%", metrics.total_return);
    println!("   Sharpe Ratio:      {:.2}", metrics.sharpe_ratio);
    println!("   Max Drawdown:      {:.2}%", metrics.max_drawdown);
    println!("   Calmar Ratio:      {:.2}", metrics.calmar_ratio);
    println!("   Profit Factor:     {:.2}", metrics.profit_factor);

    println!("\n✅ Wave D comparison backtest complete!");
    println!("\n{}", "=".repeat(70));

    Ok(())
}