# Phase 1: Single Neuron Test Configuration
# Polygon API -> Event Bus -> DQN Model -> Log Output

[strategy]
# Use AI Orchestration Strategy with DQN-only mode
strategy_type = "ai_orchestration"
strategy_id = "phase1_dqn_test"

[backtesting]
# Test configuration
start_time = "2024-01-02T09:30:00Z"
end_time = "2024-01-02T16:00:00Z"
initial_capital = 100000.0
commission_bps = 1.0
slippage_bps = 0.5
tick_size = 0.01

# Single symbol for testing
symbols = ["AAPL"]

# Reduced latency for testing
strategy_to_exchange_latency_us = 100
exchange_to_strategy_latency_us = 100

# Disable complex features for Phase 1
enable_market_impact = false
enable_queue_position = false
enable_latency_modeling = false

[data_source]
# PHASE 1: Deterministic testing with canned data
mode = "FromFile"
path = "tests/fixtures/canned_aapl_data.jsonl"

[ai_orchestration]
# PHASE 1: DQN-ONLY MODE
enabled_models.dqn_enabled = true
enabled_models.tggn_enabled = false
enabled_models.tft_enabled = false
enabled_models.mamba_enabled = false
enabled_models.liquid_enabled = false

# DQN Configuration
[ai_orchestration.dqn_config]
state_size = 20
learning_rate = 0.001
batch_size = 32
memory_size = 10000
epsilon = 0.1
epsilon_decay = 0.995
epsilon_min = 0.01

# Model weights (DQN = 1.0, others = 0.0)
[ai_orchestration.model_weights]
dqn_weight = 1.0
tggn_weight = 0.0
tft_weight = 0.0
mamba_weight = 0.0
liquid_weight = 0.0

# Risk limits
[ai_orchestration.risk_limits]
max_position_pct = 0.05
max_daily_loss = 0.02
max_trades_per_hour = 10
stop_loss_pct = 0.01
take_profit_pct = 0.02

# Performance requirements
max_inference_latency_us = 1000  # 1ms max for Phase 1



[logging]
# Enhanced logging for Phase 1 debugging
level = "debug"
filter = "backtesting=debug,ai_orchestration=trace"

# Log specific events for Phase 1 validation
log_market_data = true
log_ai_predictions = true
log_signal_generation = true
log_order_events = true

[validation]
# Phase 1 success criteria - ROBUST validation decoupled from model predictions
expected_log_messages = [
    "PIPELINE_SUCCESS: data_ingestion_complete",
    "PIPELINE_SUCCESS: feature_extraction_complete", 
    "PIPELINE_SUCCESS: DQN_inference_complete",
    "PIPELINE_SUCCESS: signal_processing_complete"
]

# Performance thresholds
max_event_processing_time_us = 1000
min_market_data_events = 20  # Reduced for canned data
expected_pipeline_completions = 10

# Deterministic test expectations
expected_canned_events = 20  # Number of events in canned data file
timeout_seconds = 10  # Reduced timeout for file-based testing

[database]
# Use lightweight SQLite for Phase 1 testing
database_url = "sqlite:///tmp/phase1_test.db"
auto_migrate = true
log_queries = true

[output]
# Save Phase 1 results for analysis
save_results = true
results_file = "/tmp/phase1_test_results.json"
save_performance_metrics = true
save_ai_predictions = true
save_market_data_sample = true