308 lines
9.1 KiB
YAML
308 lines
9.1 KiB
YAML
run:
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name: baseline_feature_target_model_registry
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seed: 42
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output_dir: artifacts/runs
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# ------------------------------------------------------------
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# DATA
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# ------------------------------------------------------------
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data:
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path: bars_bybit_1min_2026-03-01_2026-05-11.parquet
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time_col: time
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symbol_col: symbol
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symbol_candle: INDEX
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candle_rule: 1min
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min_coverage: 0.98
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max_symbols: 120
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required_cols:
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- open
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- high
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- low
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- close
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- buy_volume
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- sell_volume
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# ------------------------------------------------------------
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# SPLIT
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# ------------------------------------------------------------
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split:
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train_size: 0.70
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test_size: 0.15
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valid_size: 0.15
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# ------------------------------------------------------------
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# BACKTEST
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# ------------------------------------------------------------
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backtest:
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fee_rate: 0.0005
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initial_state: 1
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# ------------------------------------------------------------
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# FEATURE SETS
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# ------------------------------------------------------------
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feature_sets:
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A_market_basic:
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description: "returns, range, ATR, volatility, volume imbalance"
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builders:
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- name: market_basic
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params:
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return_lags: [1, 3, 5, 15, 30, 60, 120]
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range_windows: [14, 30, 60, 120]
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atr_windows: [14, 60, 120]
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vol_windows: [30, 60, 120, 360]
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volume_windows: [30, 60, 120]
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B_primitive_pressure:
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description: "primitive continuous pressure parameters from OHLCV"
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builders:
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- name: primitive_pressure
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params:
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price_lags: [13, 34, 55, 144, 233]
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force_lag: 34
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volume_norm_win: 1440
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C_mode_values:
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description: "continuous mode values: level, speed, accel, cycle, persistence"
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builders:
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- name: mode_values
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params:
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speed_ema: 45
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speed_lag: 5
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cycle_win: 360
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persist_win: 120
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D_state_features:
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description: "discrete state features derived from mode values"
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builders:
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- name: state_features
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params:
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deadband: 0.05
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cycle_deadband: 0.15
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persist_deadband: 0.20
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E_agreement_features:
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description: "agreement features derived from state features"
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builders:
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- name: agreement_features
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params:
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state:
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deadband: 0.05
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cycle_deadband: 0.15
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persist_deadband: 0.20
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F_long_context_features:
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description: "long-context features derived from primitive pressures"
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builders:
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- name: long_context
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params:
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ema_spans: [120, 240, 360, 720, 1440]
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trend_lags: [120, 240, 360, 720]
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cycle_wins: [360, 720, 1440]
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persist_wins: [240, 480, 720, 1440]
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fast_span: 45
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# ----------------------------------------------------------
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# BIG NOTEBOOK-LIKE FEATURE SETS
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# ----------------------------------------------------------
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# These builders use full market matrices px/buy/sell, not only INDEX OHLC.
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# They are implemented in:
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# src/ml_crypto_lab/features/fusion_factory.py
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# src/ml_crypto_lab/features/fusion_builders.py
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B_big_primitive_pressure:
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description: "large primitive pressure table: fusion_score, fusion_force, volume_pressure, index_pressure and additional pressures"
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builders:
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- name: advanced_fusion_params
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params:
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prefix: "b_big__"
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factory:
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symbol_candle: INDEX
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max_symbols: 120
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include_index_in_cross_section: false
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vol_use_mean: true
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price_z_wins: [1600]
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price_breadth_wins: [500, 900]
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vol_w_list: [120, 180]
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vol_p_list: [1]
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vol_roll_sets: [[1600], [2000], [2400]]
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force_winsor_q_list: [0.15]
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force_base_weight_list: [2.0]
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q_weight_scale_list: [0.70]
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force_w_price_list: [0.75]
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use_base_imbalance_list: [true]
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z_roll_wins: [400, 900, 1400, 1900]
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breadth_wins: [100, 170, 240, 310, 380]
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pct_up_norms: [50]
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pct_down_norms: [50]
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score_w_list: [60]
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score_p_list: [2]
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score_roll_sets: [[1400]]
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C_big_mode_values:
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description: "large continuous mode values derived from B_big primitive pressures"
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builders:
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- name: advanced_fusion_mode_values
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params:
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prefix: "c_big__"
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factory:
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symbol_candle: INDEX
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max_symbols: 120
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price_z_wins: [1600]
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price_breadth_wins: [500, 900]
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vol_w_list: [120, 180]
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vol_roll_sets: [[1600], [2000], [2400]]
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D_big_state_features:
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description: "large discrete *_state features derived from B_big/C_big"
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builders:
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- name: advanced_fusion_states
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params:
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prefix: "d_big__"
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factory:
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symbol_candle: INDEX
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max_symbols: 120
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price_z_wins: [1600]
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price_breadth_wins: [500, 900]
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vol_w_list: [120, 180]
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vol_roll_sets: [[1600], [2000], [2400]]
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E_big_agreement_features:
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description: "large agreement features by mode and by parameter"
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builders:
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- name: advanced_fusion_agreements
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params:
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prefix: "e_big__"
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factory:
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symbol_candle: INDEX
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max_symbols: 120
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price_z_wins: [1600]
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price_breadth_wins: [500, 900]
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vol_w_list: [120, 180]
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vol_roll_sets: [[1600], [2000], [2400]]
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F_big_long_context_features:
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description: "large long-context feature set derived from primitive pressures"
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builders:
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- name: advanced_fusion_long_context
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params:
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prefix: "f_big__"
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factory:
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symbol_candle: INDEX
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max_symbols: 120
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price_z_wins: [1600]
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price_breadth_wins: [500, 900]
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vol_w_list: [120, 180]
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vol_roll_sets: [[1600], [2000], [2400]]
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lctx_ema_spans: [120, 240, 360, 720, 1440]
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lctx_trend_lags: [120, 240, 360, 720]
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lctx_cycle_wins: [360, 720, 1440]
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lctx_persist_wins: [240, 480, 720, 1440]
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lctx_breakout_wins: [360, 720, 1440]
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lctx_impulse_lags: [120, 240, 360, 720]
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lctx_max_output_cols: 10000
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ALL_big_designed_features:
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description: "full big designed table: params + modes + states + agreements + long_context"
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builders:
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- name: advanced_fusion_full
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params:
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prefix: "sig_big__"
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factory:
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symbol_candle: INDEX
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max_symbols: 120
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price_z_wins: [1600]
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price_breadth_wins: [500, 900]
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vol_w_list: [120, 180]
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vol_roll_sets: [[1600], [2000], [2400]]
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lctx_max_output_cols: 10000
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# ------------------------------------------------------------
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# TARGET SETS
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# ------------------------------------------------------------
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target_sets:
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zz_long:
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builders:
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- name: zigzag
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params:
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pct_list: [0.018, 0.022, 0.026, 0.030, 0.035, 0.040]
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atr_mult_list: [1.5, 2.0, 2.5]
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min_bars_list: [180, 240, 360, 480, 720]
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atr_window: 60
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initial_state: 1
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future_mean:
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builders:
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- name: future_mean
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params:
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horizon_list: [360, 720, 1080, 1440]
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min_move_pct_list: [0.004, 0.0065, 0.010]
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atr_mult_list: [0.75, 1.25, 1.75]
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fee_rate: 0.0005
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fee_safety_rate: 0.00075
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atr_window: 60
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initial_state: 1
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future_return:
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builders:
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- name: future_return
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params:
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horizon_list: [240, 360, 720, 1080, 1440]
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min_move_pct_list: [0.005, 0.008, 0.012]
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atr_mult_list: [1.0, 1.5, 2.0]
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fee_rate: 0.0005
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fee_safety_rate: 0.00075
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atr_window: 60
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initial_state: 1
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# ------------------------------------------------------------
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# MODELS
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# ------------------------------------------------------------
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models:
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logreg:
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name: sklearn_logreg
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params:
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C: 0.5
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max_iter: 2000
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class_weight: balanced
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extra_trees:
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name: sklearn_extra_trees
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params:
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n_estimators: 400
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max_depth: 12
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min_samples_leaf: 60
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max_features: sqrt
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n_jobs: -1
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random_state: 42
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hist_gb:
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name: sklearn_hist_gradient_boosting
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params:
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max_iter: 250
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learning_rate: 0.05
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max_leaf_nodes: 31
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l2_regularization: 0.01
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random_state: 42
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# ------------------------------------------------------------
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# EXPERIMENT MATRIX
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# ------------------------------------------------------------
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experiment_matrix:
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# Old compact sets are still available. For big notebook-like experiments use the *_big sets.
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feature_sets: [A_market_basic, B_big_primitive_pressure, C_big_mode_values, D_big_state_features, E_big_agreement_features, F_big_long_context_features, ALL_big_designed_features]
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target_sets: [zz_long, future_mean, future_return]
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models: [logreg, extra_trees, hist_gb]
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training:
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max_targets_per_set: null
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save_predictions: true
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save_models: true
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ensemble:
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enabled: true
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methods:
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- majority_vote
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- score_average
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