# DQN Hyperopt Profile — PSO search space definition
# All bounds are [min, max] ranges. The adapter reads these at runtime.
# To constrain the search space, narrow the ranges here — no code changes needed.

[search_space]
# Base parameters
learning_rate = [0.00001, 0.0003]       # log scale in adapter
batch_size = [64, 512]
gamma = [0.90, 0.99]                    # wider range — v_range computed dynamically from gamma
buffer_size = [50000, 100000]           # log scale in adapter
max_position_absolute = [1.0, 4.0]
huber_delta = [10.0, 40.0]             # log scale in adapter
entropy_coefficient = [0.05, 0.5]
transaction_cost_multiplier = [0.5, 2.0]
per_alpha = [0.4, 0.8]
per_beta_start = [0.2, 0.6]

# Rainbow DQN extensions
v_range = [1.0, 1.0]                   # IGNORED: v_range now computed dynamically from gamma
noisy_sigma_init = [0.1, 1.0]          # log scale in adapter
dueling_hidden_dim = [128, 512]
n_steps = [3, 5]
num_atoms = [51, 101]                   # 51=C51 paper minimum, 101=higher resolution (2x slower but better signal)

# Weight decay
weight_decay = [0.0001, 0.01]          # log scale in adapter

# Kelly risk parameters
kelly_fractional = [0.25, 0.75]
kelly_max_fraction = [0.1, 0.5]

# Volatility
volatility_window = [10, 30]

# Soft update
tau = [0.005, 0.01]                    # log scale in adapter

# Network sizing
hidden_dim_base = [128, 256]           # capped at production default: 512 is 4x slower for marginal benefit

# CQL regularization
cql_alpha = [0.0, 1.0]

# Training dynamics
lr_decay_type = [0, 2]                 # discrete: 0=constant, 1=linear, 2=cosine
dsr_eta = [0.001, 0.05]               # log scale in adapter
minimum_profit_factor = [1.1, 2.0]

# Exploration
count_bonus_coefficient = [0.0, 0.3]

# Risk-adjusted returns
sharpe_weight = [0.0, 0.5]

# Branching DQN
branch_hidden_dim = [64, 256]

# Gradient accumulation
gradient_accumulation_steps = [1, 1]   # fixed at 1 for now

# IQN dual-head
iqn_lambda = [0.0, 2.0]

# Spectral normalization
spectral_norm_sigma_max = [1.0, 10.0]

# C51 warmup
c51_warmup_epochs = [0, 10]

# HER ratio
her_ratio = [0.0, 0.8]

# Composite reward weights
w_dsr = [0.1, 2.0]
w_pnl = [0.0, 1.0]
w_dd = [0.0, 5.0]
w_idle = [0.0, 0.1]
dd_threshold = [0.005, 0.03]            # HFT: tight drawdown tolerance (0.5%-3%)
loss_aversion = [1.0, 3.0]
time_decay_rate = [0.0001, 0.005]

# Trade conviction filter
q_gap_threshold = [0.0, 0.5]          # 0.0=trade every bar, 0.5=high conviction only

[experience]
initial_capital = 100000.0

[experience.fill_simulation]
ioc_fill_prob = 0.85
limit_fill_min = 0.30
limit_fill_max = 0.80
spread_cost_frac = 0.50
spread_capture_frac = 0.50

[risk]
q_clip_min = -200.0
q_clip_max = 200.0

[reward]
w_dsr = 1.0
w_pnl = 0.3
w_dd = 1.0
w_idle = 0.01
dd_threshold = 0.01
loss_aversion = 1.5
time_decay_rate = 0.0005
q_gap_threshold = 0.1

[fixed]

[pso]
swarm_size = 20
max_iterations = 50
inertia = 0.7
cognitive = 1.5
social = 1.5

# Two-phase hyperopt: Phase 1 fixes architecture to small network,
# searches only learning dynamics (~15D). Phase 2 fixes best dynamics
# from Phase 1 JSON, searches architecture (~5D).
# See: docs/superpowers/specs/2026-03-22-two-phase-hyperopt-design.md
[phase_fast]
hidden_dim_base = 128
num_atoms = 101
branch_hidden_dim = 64
dueling_hidden_dim = 128
v_range = 1.0
w_dsr = 1.0
w_pnl = 0.3
w_dd = 1.0
w_idle = 0.01
dd_threshold = 0.01
loss_aversion = 1.5
time_decay_rate = 0.0005
q_gap_threshold = 0.1