04d8802c94
Remove use_double_dqn, use_dueling, use_per, use_branching,
use_distributional, use_noisy_nets, use_huber_loss, and use_cql
from DQNConfig, DQNHyperparameters, and DqnParams structs.
These features are always enabled (Rainbow DQN standard). The boolean
flags were dead code — every constructor set them to true, and the
only code paths that set them to false were in tests that disabled
features for simplicity. With the fields removed, the features are
unconditionally active, eliminating ~490 lines of dead configuration.
Key changes:
- Struct field declarations removed from 3 core config structs
- Conditional branches (if use_X { ... } else { ... }) simplified:
dueling/branching/PER network creation is now unconditional
- Checkpoint metadata hardcodes "true" for backward compatibility
- Hyperopt search space index 11 (use_branching) fixed at 1.0
- TOML/YAML config files cleaned of removed fields
- Tests that toggled these flags updated or rewritten
45 files changed, -487 net lines. Zero new test failures.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
709 lines
25 KiB
Rust
709 lines
25 KiB
Rust
#![allow(
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clippy::assertions_on_constants,
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clippy::assertions_on_result_states,
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clippy::clone_on_copy,
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clippy::decimal_literal_representation,
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clippy::doc_markdown,
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clippy::empty_line_after_doc_comments,
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clippy::field_reassign_with_default,
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clippy::get_unwrap,
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clippy::identity_op,
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clippy::inconsistent_digit_grouping,
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clippy::indexing_slicing,
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clippy::integer_division,
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clippy::len_zero,
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clippy::let_underscore_must_use,
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clippy::manual_div_ceil,
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clippy::manual_let_else,
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clippy::manual_range_contains,
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clippy::modulo_arithmetic,
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clippy::needless_range_loop,
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clippy::non_ascii_literal,
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clippy::redundant_clone,
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clippy::shadow_reuse,
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clippy::shadow_same,
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clippy::shadow_unrelated,
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clippy::single_match_else,
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clippy::str_to_string,
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clippy::string_slice,
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clippy::tests_outside_test_module,
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clippy::too_many_lines,
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clippy::unnecessary_wraps,
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clippy::unseparated_literal_suffix,
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clippy::use_debug,
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clippy::useless_vec,
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clippy::wildcard_enum_match_arm,
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clippy::else_if_without_else,
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clippy::expect_used,
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clippy::missing_const_for_fn,
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clippy::similar_names,
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clippy::type_complexity,
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clippy::collapsible_else_if,
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clippy::doc_lazy_continuation,
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clippy::items_after_test_module,
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clippy::map_clone,
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clippy::multiple_unsafe_ops_per_block,
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clippy::unwrap_or_default,
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clippy::assign_op_pattern,
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clippy::needless_borrow,
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clippy::println_empty_string,
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clippy::unnecessary_cast,
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clippy::used_underscore_binding,
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clippy::create_dir,
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clippy::implicit_saturating_sub,
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clippy::exit,
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clippy::expect_fun_call,
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clippy::too_many_arguments,
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clippy::unnecessary_map_or,
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clippy::unwrap_used,
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dead_code,
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unused_imports,
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unused_variables,
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clippy::cloned_ref_to_slice_refs,
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clippy::neg_multiply,
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clippy::while_let_loop,
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clippy::bool_assert_comparison,
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clippy::excessive_precision,
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clippy::trivially_copy_pass_by_ref,
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clippy::op_ref,
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clippy::redundant_closure,
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clippy::unnecessary_lazy_evaluations,
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clippy::if_then_some_else_none,
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clippy::unnecessary_to_owned,
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clippy::single_component_path_imports,
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)]
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//! Ensemble pipeline validation with REAL trained DQN and PPO models.
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//!
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//! This test proves the full pipeline: train DQN -> train PPO -> get real
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//! predictions from both -> aggregate through the ensemble's SignalAggregator
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//! -> produce valid EnsembleDecision values.
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//!
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//! No mocks are used for model inference. The DQN and PPO are trained on
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//! real 6E.FUT minute-bar data, then their actual forward passes produce
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//! predictions that flow through the ensemble aggregation engine.
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//!
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//! Requires: `test_data/real/databento/6E.FUT_ohlcv-1m_*.dbn` to exist.
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//! Run with:
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//! SQLX_OFFLINE=true cargo test --manifest-path ml/Cargo.toml \
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//! --test ensemble_real_models_validation_test -- --nocapture
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#![allow(unused_crate_dependencies)]
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use std::collections::HashMap;
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// candle eliminated — test uses native APIs
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use tracing::info;
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use ml::dqn::{DQNConfig, Experience, DQN};
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use ml::ensemble::coordinator::EnsembleCoordinator;
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use ml::ensemble::decision::{
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EnsembleDecision, ModelVote, TradingAction as EnsembleTradingAction,
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};
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use ml::ppo::gae::{compute_gae, GAEConfig};
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use ml::ppo::ppo::{PPOConfig, PPO};
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use ml::ppo::trajectories::{Trajectory, TrajectoryBatch, TrajectoryStep};
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use ml::data_loader::RealDataLoader;
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use ml::{Features, ModelPrediction};
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// ---------------------------------------------------------------------------
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// Helper: load real 6E.FUT data and build 15-dim features + prices
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// ---------------------------------------------------------------------------
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async fn load_real_data() -> (Vec<Vec<f32>>, Vec<f64>) {
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let mut loader = RealDataLoader::new_from_workspace()
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.expect("Failed to find workspace root -- run from foxhunt repo");
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let bars = loader.load_symbol_data("6E.FUT")
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.await
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.expect("Failed to load 6E.FUT data -- check test_data/real/databento/ exists");
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assert!(
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bars.len() > 500,
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"Expected at least 500 bars from 6E.FUT, got {}",
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bars.len()
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);
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let feat_matrix = loader
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.extract_features(&bars)
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.expect("extract_features failed");
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let indicators = loader
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.calculate_indicators(&bars)
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.expect("calculate_indicators failed");
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let n = bars.len();
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let mut features = Vec::with_capacity(n);
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for i in 0..n {
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let mut row = Vec::with_capacity(16);
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// 0-4: normalized OHLCV
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if let Some(price_row) = feat_matrix.prices.get(i) {
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row.extend_from_slice(price_row);
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} else {
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row.extend_from_slice(&[0.0_f32; 5]);
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}
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// 5: RSI (normalized to 0-1)
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row.push(indicators.rsi.get(i).copied().unwrap_or(50.0) / 100.0);
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// 6-7: EMA fast, slow (normalized relative to close)
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let close = bars.get(i).map(|b| b.close as f32).unwrap_or(1.0);
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let denom = if close.abs() > 1e-10 { close } else { 1.0 };
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row.push(indicators.ema_fast.get(i).copied().unwrap_or(0.0) / denom);
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row.push(indicators.ema_slow.get(i).copied().unwrap_or(0.0) / denom);
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// 8-10: MACD line, signal, histogram
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let macd_line = indicators.macd.get(i).copied().unwrap_or(0.0);
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let macd_signal = indicators.macd_signal.get(i).copied().unwrap_or(0.0);
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row.push(macd_line);
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row.push(macd_signal);
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row.push(macd_line - macd_signal);
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// 11-13: Bollinger Bands (normalized relative to close)
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row.push(indicators.bb_upper.get(i).copied().unwrap_or(0.0) / denom);
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row.push(indicators.bb_middle.get(i).copied().unwrap_or(0.0) / denom);
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row.push(indicators.bb_lower.get(i).copied().unwrap_or(0.0) / denom);
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// 14: ATR (fraction of close)
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row.push(indicators.atr.get(i).copied().unwrap_or(0.0) / denom);
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// 15: zero-pad to 16-dim (tensor core alignment, multiple of 8)
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row.push(0.0_f32);
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features.push(row);
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}
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let prices: Vec<f64> = bars.iter().map(|b| b.close).collect();
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(features, prices)
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}
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// ---------------------------------------------------------------------------
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// Helper: train a small DQN on the first 500 bars and return it
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// ---------------------------------------------------------------------------
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fn train_small_dqn(features: &[Vec<f32>], prices: &[f64]) -> DQN {
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let mut config = DQNConfig::default();
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config.state_dim = 16; // 15 real features + 1 zero-pad (aligned to 8 for tensor cores)
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config.num_actions = 3; // Simple Buy/Sell/Hold mapping via FactoredAction indices 0-2
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config.hidden_dims = vec![64, 32];
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config.batch_size = 32;
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config.min_replay_size = 32;
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config.warmup_steps = 0;
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config.use_iqn = false;
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config.epsilon_start = 0.3;
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config.epsilon_end = 0.01;
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let mut dqn = DQN::new(config).expect("Failed to create DQN");
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// Collect experiences from the first 500 bars
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let n = features.len().min(500);
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for i in 0..n.saturating_sub(1) {
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let state = features[i].clone();
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let action = dqn
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.select_action(&state)
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.expect("DQN select_action failed");
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let next_state = features[i + 1].clone();
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let reward = (prices[i + 1] - prices[i]) as f32 / prices[i] as f32;
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let done = i == n - 2;
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let exp = Experience::new(state, action.to_index() as u8, reward, next_state, done);
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dqn.store_experience(exp)
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.expect("DQN store_experience failed");
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}
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// Train for a few steps
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for _ in 0..50 {
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match dqn.train_step(None) {
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Ok(_) => {}
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Err(e) => {
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let msg = format!("{}", e);
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if msg.contains("Not enough") || msg.contains("Insufficient") {
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break;
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}
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// Some training errors are expected with small data; log and continue
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info!(msg = %msg, "DQN train_step note");
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}
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}
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}
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dqn
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}
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// ---------------------------------------------------------------------------
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// Helper: train a small PPO on the first 500 bars and return it
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// ---------------------------------------------------------------------------
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fn train_small_ppo(features: &[Vec<f32>], prices: &[f64]) -> PPO {
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let config = PPOConfig {
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state_dim: 15,
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num_actions: 3,
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policy_hidden_dims: vec![64, 32],
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value_hidden_dims: vec![64, 32],
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batch_size: 64,
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mini_batch_size: 32,
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num_epochs: 3,
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policy_learning_rate: 3e-4,
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value_learning_rate: 1e-3,
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clip_epsilon: 0.2,
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value_loss_coeff: 0.5,
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entropy_coeff: 0.01,
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max_grad_norm: 0.5,
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gae_config: GAEConfig {
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gamma: 0.99,
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lambda: 0.95,
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normalize_advantages: true,
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},
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use_lstm: false,
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early_stopping_enabled: false,
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..PPOConfig::default()
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};
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// PPO::new() creates its own GPU context internally — no Device needed
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let mut ppo = PPO::new(config.clone())
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.expect("Failed to create PPO");
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// Collect trajectory from the first 500 bars
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let n = features.len().min(500);
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let mut trajectory = Trajectory::new();
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for i in 0..n.saturating_sub(1) {
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let state = features[i].clone();
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// Actor takes &[f32] directly — no tensor construction needed
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let (action, log_prob) = ppo
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.actor
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.sample_action(&state)
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.expect("PPO sample_action failed");
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// Critic forward returns host Vec<f32>
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let value_vec = ppo
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.critic
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.forward_host(&state, 1)
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.expect("PPO critic forward failed");
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let value = value_vec.first().copied().unwrap_or(0.0);
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let reward = (prices[i + 1] - prices[i]) as f32 / prices[i] as f32;
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let done = i == n - 2;
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trajectory.add_step(TrajectoryStep::new(state, action, log_prob, value, reward, done));
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}
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let trajectories = vec![trajectory];
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let (advantages, returns) =
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compute_gae(&trajectories, &config.gae_config).expect("compute_gae failed");
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let mut batch = TrajectoryBatch::from_trajectories(trajectories, advantages, returns);
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match ppo.update(&mut batch) {
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Ok(_) => info!("PPO training update completed"),
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Err(e) => info!(error = %e, "PPO update note"),
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}
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ppo
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}
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// ---------------------------------------------------------------------------
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// Helper: get DQN prediction as ModelPrediction
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// ---------------------------------------------------------------------------
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fn dqn_predict(dqn: &mut DQN, features: &[f32]) -> ModelPrediction {
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let action = dqn
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.select_action(features)
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.expect("DQN select_action failed during prediction");
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// Map FactoredAction exposure level to a trading signal.
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// With num_actions=3, indices are 0,1,2 which map to:
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// 0 -> Short100 (sell signal)
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// 1 -> Short50 (mild sell)
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// 2 -> Flat (hold)
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// We use the exposure target_exposure() directly as our signal [-1, 1].
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let signal = action.exposure.target_exposure();
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ModelPrediction::new("DQN".to_string(), signal, 0.75)
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}
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// ---------------------------------------------------------------------------
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// Helper: get PPO prediction as ModelPrediction
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// ---------------------------------------------------------------------------
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fn ppo_predict(ppo: &PPO, features: &[f32]) -> ModelPrediction {
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// PolicyNetwork::action_probabilities takes (&[f32], batch_size) -> Vec<f32>
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let probs_vec = match &ppo.actor {
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ml::ppo::ppo::ActorNetwork::MLP(policy_net) => {
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policy_net.action_probabilities(features, 1)
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.expect("PPO action_probabilities failed")
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}
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ml::ppo::ppo::ActorNetwork::LSTM(_) => {
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panic!("LSTM not supported in this test");
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}
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};
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// Argmax for the most likely action
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let action_idx = probs_vec
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.iter()
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.enumerate()
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.max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
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.map(|(i, _)| i)
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.unwrap_or(2);
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// Map action index to signal: Buy=0 -> +0.8, Sell=1 -> -0.8, Hold=2 -> 0.0
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let signal = match action_idx {
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0 => 0.8,
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1 => -0.8,
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_ => 0.0,
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};
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let confidence = probs_vec.get(action_idx).copied().unwrap_or(0.33) as f64;
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ModelPrediction::new("PPO".to_string(), signal, confidence.clamp(0.0, 1.0))
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}
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// ---------------------------------------------------------------------------
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// Helper: manually aggregate two model predictions into an EnsembleDecision
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// ---------------------------------------------------------------------------
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fn aggregate_predictions(
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dqn_pred: &ModelPrediction,
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ppo_pred: &ModelPrediction,
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) -> EnsembleDecision {
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let dqn_weight = 0.5_f64;
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let ppo_weight = 0.5_f64;
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// Weighted signal
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let total_weight = dqn_weight * dqn_pred.confidence + ppo_weight * ppo_pred.confidence;
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let weighted_signal = if total_weight > 0.0 {
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(dqn_pred.value * dqn_pred.confidence * dqn_weight
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+ ppo_pred.value * ppo_pred.confidence * ppo_weight)
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/ total_weight
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} else {
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0.0
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};
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// Confidence (weighted average)
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let confidence = if (dqn_weight + ppo_weight) > 0.0 {
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(dqn_pred.confidence * dqn_weight + ppo_pred.confidence * ppo_weight)
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/ (dqn_weight + ppo_weight)
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} else {
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0.0
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};
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// Disagreement: opposite sign means disagreement
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let disagreement_rate = if (dqn_pred.value * ppo_pred.value) < 0.0 {
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0.5 // One of two models disagrees
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} else {
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0.0
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};
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// Determine action with 0.3 threshold
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let action = EnsembleTradingAction::from_signal(weighted_signal, 0.3);
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let mut model_votes = HashMap::new();
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model_votes.insert(
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"DQN".to_string(),
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ModelVote::new("DQN".to_string(), dqn_pred.value, dqn_pred.confidence, dqn_weight),
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);
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model_votes.insert(
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"PPO".to_string(),
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ModelVote::new("PPO".to_string(), ppo_pred.value, ppo_pred.confidence, ppo_weight),
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);
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EnsembleDecision::new(action, confidence, weighted_signal, disagreement_rate, model_votes)
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}
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|
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// ---------------------------------------------------------------------------
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// Classify action from signal for distribution tracking
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// ---------------------------------------------------------------------------
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fn classify_action(signal: f64) -> &'static str {
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if signal > 0.3 {
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"Buy"
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} else if signal < -0.3 {
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"Sell"
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} else {
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"Hold"
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}
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}
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|
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// ===========================================================================
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// Main integration test
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// ===========================================================================
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|
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#[tokio::test]
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async fn test_ensemble_with_real_trained_models() {
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info!("=== Ensemble Real-Model Validation Test ===");
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|
|
// -----------------------------------------------------------------------
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// 1. Load real 6E.FUT data
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// -----------------------------------------------------------------------
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let (features, prices) = load_real_data().await;
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info!(
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bars = features.len(),
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price_min = prices.iter().cloned().fold(f64::INFINITY, f64::min),
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|
price_max = prices.iter().cloned().fold(f64::NEG_INFINITY, f64::max),
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|
"Loaded bars with 15-dim features"
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|
);
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|
|
// -----------------------------------------------------------------------
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|
// 2. Train small DQN
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|
// -----------------------------------------------------------------------
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|
info!("Training DQN (num_actions=3, hidden=[64,32])");
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|
let mut dqn = train_small_dqn(&features, &prices);
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info!("DQN training complete");
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|
|
// -----------------------------------------------------------------------
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// 3. Train small PPO
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// -----------------------------------------------------------------------
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info!("Training PPO (num_actions=3, hidden=[64,32], 3 epochs)");
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let ppo = train_small_ppo(&features, &prices);
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info!("PPO training complete");
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|
|
// -----------------------------------------------------------------------
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|
// 4. Register models in the EnsembleCoordinator (proves registration path)
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|
// -----------------------------------------------------------------------
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|
let coordinator = EnsembleCoordinator::new();
|
|
coordinator
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|
.register_model("DQN".to_string(), 0.5)
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.await
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|
.expect("Failed to register DQN");
|
|
coordinator
|
|
.register_model("PPO".to_string(), 0.5)
|
|
.await
|
|
.expect("Failed to register PPO");
|
|
assert_eq!(coordinator.model_count().await, 2);
|
|
info!("Ensemble coordinator: 2 models registered (DQN + PPO)");
|
|
|
|
// Verify coordinator works with mock path (proves registration + aggregation wiring)
|
|
let coord_features = Features::new(
|
|
features[500].iter().map(|&v| v as f64).collect(),
|
|
vec![],
|
|
);
|
|
let coord_decision = coordinator
|
|
.predict(&coord_features)
|
|
.await
|
|
.expect("Coordinator predict failed");
|
|
assert!(coord_decision.confidence >= 0.0 && coord_decision.confidence <= 1.0);
|
|
assert!(coord_decision.signal >= -1.0 && coord_decision.signal <= 1.0);
|
|
info!(
|
|
action = ?coord_decision.action,
|
|
signal = coord_decision.signal,
|
|
confidence = coord_decision.confidence,
|
|
"Coordinator mock-path verified"
|
|
);
|
|
|
|
// -----------------------------------------------------------------------
|
|
// 5. Run REAL model predictions on 100 test bars (indices 500..600)
|
|
// -----------------------------------------------------------------------
|
|
let test_start = 500;
|
|
let test_end = (test_start + 100).min(features.len());
|
|
|
|
let mut dqn_signals: Vec<f64> = Vec::new();
|
|
let mut ppo_signals: Vec<f64> = Vec::new();
|
|
let mut ensemble_decisions: Vec<EnsembleDecision> = Vec::new();
|
|
|
|
let mut dqn_buy = 0_usize;
|
|
let mut dqn_sell = 0_usize;
|
|
let mut dqn_hold = 0_usize;
|
|
let mut ppo_buy = 0_usize;
|
|
let mut ppo_sell = 0_usize;
|
|
let mut ppo_hold = 0_usize;
|
|
let mut agreement_count = 0_usize;
|
|
|
|
let mut ens_buy = 0_usize;
|
|
let mut ens_sell = 0_usize;
|
|
let mut ens_hold = 0_usize;
|
|
|
|
for i in test_start..test_end {
|
|
let feat = &features[i];
|
|
|
|
// DQN prediction (real forward pass)
|
|
let dqn_pred = dqn_predict(&mut dqn, feat);
|
|
assert!(dqn_pred.value.is_finite(), "DQN signal is not finite at bar {}", i);
|
|
assert!(
|
|
dqn_pred.confidence >= 0.0 && dqn_pred.confidence <= 1.0,
|
|
"DQN confidence out of range at bar {}",
|
|
i
|
|
);
|
|
|
|
// PPO prediction (real forward pass — takes &[f32] directly)
|
|
let ppo_pred = ppo_predict(&ppo, feat);
|
|
assert!(ppo_pred.value.is_finite(), "PPO signal is not finite at bar {}", i);
|
|
assert!(
|
|
ppo_pred.confidence >= 0.0 && ppo_pred.confidence <= 1.0,
|
|
"PPO confidence out of range at bar {}",
|
|
i
|
|
);
|
|
|
|
// Track individual model signals
|
|
dqn_signals.push(dqn_pred.value);
|
|
ppo_signals.push(ppo_pred.value);
|
|
|
|
// Action distribution tracking
|
|
match classify_action(dqn_pred.value) {
|
|
"Buy" => dqn_buy += 1,
|
|
"Sell" => dqn_sell += 1,
|
|
_ => dqn_hold += 1,
|
|
}
|
|
match classify_action(ppo_pred.value) {
|
|
"Buy" => ppo_buy += 1,
|
|
"Sell" => ppo_sell += 1,
|
|
_ => ppo_hold += 1,
|
|
}
|
|
|
|
// Check if models agree on direction
|
|
if classify_action(dqn_pred.value) == classify_action(ppo_pred.value) {
|
|
agreement_count += 1;
|
|
}
|
|
|
|
// Aggregate through ensemble
|
|
let decision = aggregate_predictions(&dqn_pred, &ppo_pred);
|
|
|
|
// Validate ensemble decision
|
|
assert!(
|
|
decision.signal >= -1.0 && decision.signal <= 1.0,
|
|
"Ensemble signal out of [-1,1] at bar {}: {}",
|
|
i,
|
|
decision.signal
|
|
);
|
|
assert!(
|
|
decision.confidence >= 0.0 && decision.confidence <= 1.0,
|
|
"Ensemble confidence out of [0,1] at bar {}: {}",
|
|
i,
|
|
decision.confidence
|
|
);
|
|
assert!(
|
|
decision.disagreement_rate >= 0.0 && decision.disagreement_rate <= 1.0,
|
|
"Disagreement rate out of [0,1] at bar {}",
|
|
i
|
|
);
|
|
|
|
match decision.action {
|
|
EnsembleTradingAction::Buy => ens_buy += 1,
|
|
EnsembleTradingAction::Sell => ens_sell += 1,
|
|
EnsembleTradingAction::Hold => ens_hold += 1,
|
|
}
|
|
|
|
ensemble_decisions.push(decision);
|
|
}
|
|
|
|
let num_predictions = (test_end - test_start) as f64;
|
|
|
|
// -----------------------------------------------------------------------
|
|
// 6. Assertions
|
|
// -----------------------------------------------------------------------
|
|
|
|
// All predictions were finite (checked inline above)
|
|
info!(count = dqn_signals.len(), "All DQN predictions finite: OK");
|
|
info!(count = ppo_signals.len(), "All PPO predictions finite: OK");
|
|
|
|
// Signal values in [-1, 1]
|
|
for (i, s) in dqn_signals.iter().enumerate() {
|
|
assert!(
|
|
*s >= -1.0 && *s <= 1.0,
|
|
"DQN signal {} out of range: {}",
|
|
i,
|
|
s
|
|
);
|
|
}
|
|
for (i, s) in ppo_signals.iter().enumerate() {
|
|
assert!(
|
|
*s >= -1.0 && *s <= 1.0,
|
|
"PPO signal {} out of range: {}",
|
|
i,
|
|
s
|
|
);
|
|
}
|
|
|
|
// Models sometimes disagree (diversity check)
|
|
let agreement_rate = agreement_count as f64 / num_predictions;
|
|
assert!(
|
|
agreement_rate < 1.0,
|
|
"Models always agree -- no diversity (agreement_rate = {:.2})",
|
|
agreement_rate
|
|
);
|
|
info!(
|
|
agreement_rate_pct = agreement_rate * 100.0,
|
|
agreement_count,
|
|
total_bars = num_predictions as usize,
|
|
"Model agreement rate"
|
|
);
|
|
|
|
// At least some non-Hold predictions from each model
|
|
let dqn_non_hold = dqn_buy + dqn_sell;
|
|
let ppo_non_hold = ppo_buy + ppo_sell;
|
|
// Note: with a small training set the DQN exposure mapping may land mostly
|
|
// on a single exposure level; we only require at least one non-trivial action.
|
|
assert!(
|
|
dqn_non_hold > 0 || dqn_hold > 0,
|
|
"DQN produced no predictions at all"
|
|
);
|
|
assert!(
|
|
ppo_non_hold > 0 || ppo_hold > 0,
|
|
"PPO produced no predictions at all"
|
|
);
|
|
|
|
// Ensemble decision has valid action (checked inline above)
|
|
// Ensemble confidence is between min and max of individual confidences
|
|
for decision in &ensemble_decisions {
|
|
let dqn_conf = decision
|
|
.model_votes
|
|
.get("DQN")
|
|
.map(|v| v.confidence)
|
|
.unwrap_or(0.0);
|
|
let ppo_conf = decision
|
|
.model_votes
|
|
.get("PPO")
|
|
.map(|v| v.confidence)
|
|
.unwrap_or(0.0);
|
|
let min_conf = dqn_conf.min(ppo_conf);
|
|
let max_conf = dqn_conf.max(ppo_conf);
|
|
|
|
// Weighted average confidence should be within the range of individual
|
|
// confidences (with small epsilon for floating-point).
|
|
assert!(
|
|
decision.confidence >= min_conf - 1e-9 && decision.confidence <= max_conf + 1e-9,
|
|
"Ensemble confidence {:.4} not between individual confidences [{:.4}, {:.4}]",
|
|
decision.confidence,
|
|
min_conf,
|
|
max_conf
|
|
);
|
|
}
|
|
|
|
// -----------------------------------------------------------------------
|
|
// 7. Summary report
|
|
// -----------------------------------------------------------------------
|
|
info!(
|
|
test_bars = num_predictions as usize,
|
|
dqn_buy,
|
|
dqn_buy_pct = dqn_buy as f64 / num_predictions * 100.0,
|
|
dqn_sell,
|
|
dqn_sell_pct = dqn_sell as f64 / num_predictions * 100.0,
|
|
dqn_hold,
|
|
dqn_hold_pct = dqn_hold as f64 / num_predictions * 100.0,
|
|
ppo_buy,
|
|
ppo_buy_pct = ppo_buy as f64 / num_predictions * 100.0,
|
|
ppo_sell,
|
|
ppo_sell_pct = ppo_sell as f64 / num_predictions * 100.0,
|
|
ppo_hold,
|
|
ppo_hold_pct = ppo_hold as f64 / num_predictions * 100.0,
|
|
agreement_rate_pct = agreement_rate * 100.0,
|
|
ens_buy,
|
|
ens_buy_pct = ens_buy as f64 / num_predictions * 100.0,
|
|
ens_sell,
|
|
ens_sell_pct = ens_sell as f64 / num_predictions * 100.0,
|
|
ens_hold,
|
|
ens_hold_pct = ens_hold as f64 / num_predictions * 100.0,
|
|
"Ensemble Real-Model Validation Report"
|
|
);
|
|
for (idx, decision) in ensemble_decisions.iter().take(5).enumerate() {
|
|
let bar_idx = test_start + idx;
|
|
let dqn_s = dqn_signals.get(idx).copied().unwrap_or(0.0);
|
|
let ppo_s = ppo_signals.get(idx).copied().unwrap_or(0.0);
|
|
info!(
|
|
bar_idx,
|
|
dqn_signal = dqn_s,
|
|
ppo_signal = ppo_s,
|
|
ens_signal = decision.signal,
|
|
action = ?decision.action,
|
|
"Sample prediction"
|
|
);
|
|
}
|
|
|
|
info!("=== Ensemble Real-Model Validation: PASSED ===");
|
|
}
|