#![allow(
    clippy::assertions_on_constants,
    clippy::assertions_on_result_states,
    clippy::clone_on_copy,
    clippy::decimal_literal_representation,
    clippy::doc_markdown,
    clippy::empty_line_after_doc_comments,
    clippy::field_reassign_with_default,
    clippy::get_unwrap,
    clippy::identity_op,
    clippy::inconsistent_digit_grouping,
    clippy::indexing_slicing,
    clippy::integer_division,
    clippy::len_zero,
    clippy::let_underscore_must_use,
    clippy::manual_div_ceil,
    clippy::manual_let_else,
    clippy::manual_range_contains,
    clippy::modulo_arithmetic,
    clippy::needless_range_loop,
    clippy::non_ascii_literal,
    clippy::redundant_clone,
    clippy::shadow_reuse,
    clippy::shadow_same,
    clippy::shadow_unrelated,
    clippy::single_match_else,
    clippy::str_to_string,
    clippy::string_slice,
    clippy::tests_outside_test_module,
    clippy::too_many_lines,
    clippy::unnecessary_wraps,
    clippy::unseparated_literal_suffix,
    clippy::use_debug,
    clippy::useless_vec,
    clippy::wildcard_enum_match_arm,
    clippy::else_if_without_else,
    clippy::expect_used,
    clippy::missing_const_for_fn,
    clippy::similar_names,
    clippy::type_complexity,
    clippy::collapsible_else_if,
    clippy::doc_lazy_continuation,
    clippy::items_after_test_module,
    clippy::map_clone,
    clippy::multiple_unsafe_ops_per_block,
    clippy::unwrap_or_default,
    clippy::assign_op_pattern,
    clippy::needless_borrow,
    clippy::println_empty_string,
    clippy::unnecessary_cast,
    clippy::used_underscore_binding,
    clippy::create_dir,
    clippy::implicit_saturating_sub,
    clippy::exit,
    clippy::expect_fun_call,
    clippy::too_many_arguments,
    clippy::unnecessary_map_or,
    clippy::unwrap_used,
    dead_code,
    unused_imports,
    unused_variables,
    clippy::cloned_ref_to_slice_refs,
    clippy::neg_multiply,
    clippy::while_let_loop,
    clippy::bool_assert_comparison,
    clippy::excessive_precision,
    clippy::trivially_copy_pass_by_ref,
    clippy::op_ref,
    clippy::redundant_closure,
    clippy::unnecessary_lazy_evaluations,
    clippy::if_then_some_else_none,
    clippy::unnecessary_to_owned,
    clippy::single_component_path_imports,
)]
//! Paper trading pipeline integration test.
//!
//! Replays synthetic price data through the full pipeline:
//! Feature extraction -> Ensemble -> TradeSignal -> PaperBroker -> PnL

#![allow(unused_crate_dependencies)]
#![allow(clippy::expect_used)]
#![allow(clippy::unwrap_used)]
#![allow(clippy::panic)]
#![allow(clippy::indexing_slicing)]
#![allow(clippy::doc_markdown)]
#![allow(clippy::tests_outside_test_module)]

use ml::dqn::dqn::DQNConfig;
use ml::ensemble::adapters::dqn::DqnInferenceAdapter;
use ml::ensemble::adapters::ppo::PpoInferenceAdapter;
use ml::ensemble::inference_adapter::{FeatureVector, ModelInferenceAdapter};
use ml::ensemble::inference_ensemble::InferenceEnsemble;
use ml::ensemble::signal::TradeSignal;
use ml::paper_trading::broker::PaperBroker;
use ml::paper_trading::pnl_tracker::PnLTracker;
use ml::ppo::ppo::PPOConfig;

#[test]
fn test_paper_trading_pipeline_synthetic_data() {
    // 1. Create DQN adapter with small config
    let dqn_config = DQNConfig {
        state_dim: 51,
        num_actions: 3, // buy / hold / sell
        hidden_dims: vec![32, 32],
        ..Default::default()
    };
    let dqn_adapter =
        DqnInferenceAdapter::new(dqn_config).expect("DQN adapter should initialise");
    assert!(dqn_adapter.is_ready(), "DQN adapter must be ready");

    // 2. Create PPO adapter with matching config
    let ppo_config = PPOConfig {
        state_dim: 51,
        num_actions: 3,
        policy_hidden_dims: vec![32, 32],
        value_hidden_dims: vec![32, 32],
        ..Default::default()
    };
    let ppo_adapter =
        PpoInferenceAdapter::new(ppo_config).expect("PPO adapter should initialise");
    assert!(ppo_adapter.is_ready(), "PPO adapter must be ready");

    // 3. Build InferenceEnsemble with both adapters
    let adapters: Vec<Box<dyn ModelInferenceAdapter>> =
        vec![Box::new(dqn_adapter), Box::new(ppo_adapter)];
    let ensemble = InferenceEnsemble::new(adapters);
    assert_eq!(ensemble.ready_count(), 2, "Both models must be ready");

    // 4. Create PaperBroker with $100k cash and 1bps slippage
    let mut broker = PaperBroker::new(100_000.0, 0.0001);

    // 5. Create PnLTracker with 252-bar window
    let mut pnl_tracker = PnLTracker::new(252);

    // 6. Simulate 100 bars of synthetic price data
    let mut price = 1.0850_f64;
    let num_bars = 100_usize;

    for i in 0..num_bars {
        // Create synthetic feature vector (51-dim)
        let values: Vec<f64> = (0..51)
            .map(|j| {
                // Deterministic pseudo-features based on price and bar index
                let base = price * (1.0 + 0.001 * ((j as f64) - 25.0));
                base * (1.0 + 0.0001 * (i as f64))
            })
            .collect();

        let fv = FeatureVector {
            values,
            timestamp: 1_700_000_000_000_000 + (i as i64 * 60_000_000), // 1-min bars
        };

        // Get ensemble prediction
        let prediction = ensemble
            .predict(&fv)
            .expect("Ensemble prediction should succeed");

        // Convert to trade signal
        let signal = TradeSignal::from_prediction(&prediction, "6E.FUT");

        // Execute through broker
        let _fill = broker.execute_signal(&signal, price);

        // Record equity
        let equity = broker.equity(price);
        pnl_tracker.record_equity(equity);

        // Random-walk the price (deterministic using bar index)
        price += 0.0001 * ((i % 7) as f64 - 3.0);
    }

    // 7. Assertions
    let sharpe = pnl_tracker.rolling_sharpe();
    let drawdown = pnl_tracker.max_drawdown();
    let num_trades = broker.num_trades();
    let num_obs = pnl_tracker.num_observations();
    let cum_return = pnl_tracker.cumulative_return();

    use tracing::info;
    info!(num_bars, num_trades, sharpe, drawdown, cum_return, final_equity = broker.equity(price), price, num_obs, "Paper Trading Pipeline Summary");

    // Sharpe ratio should be finite (not NaN/Inf)
    assert!(
        sharpe.is_finite(),
        "Sharpe ratio must be finite, got {}",
        sharpe
    );

    // Drawdown should be finite and bounded [0, 1]
    assert!(
        drawdown.is_finite() && drawdown <= 1.0,
        "Drawdown must be finite and <= 1.0, got {}",
        drawdown
    );

    // At least some trades should have been executed
    assert!(
        num_trades > 0,
        "Expected at least 1 trade, got {}",
        num_trades
    );

    // PnL tracker should have recorded all 100 observations
    assert!(
        num_obs >= num_bars,
        "Expected >= {} observations, got {}",
        num_bars, num_obs
    );
}
