Edit optimizer/optimize_pso_cryptoprices-15m.toml

# PSO Optimization config for CryptoPrices Trading Strategy (15M events)
# This config defines parameter search spaces for optimizing the option-pricing based strategy
# Uses Black-Scholes (default) or Binomial pricing to detect mispriced binary options on crypto Up/Down markets
#
# NOTE: CryptoPrices strategy uses ONLY Core + AdaptiveTrailing modules.
# It does NOT use: trend, momentum, dynamic_pricing, spread, or position_sizing.
# Removed unused parameters from this config.

replay_dir = "replay_data/cryptoprices_all_slugs_20260213_210722"
slugs = ["eth-up-or-down-15m",
        "btc-up-or-down-15m",
        "sol-up-or-down-15m",
        "xrp-up-or-down-15m",
]

# Strategy type: cryptoprices
strategy_type = "cryptoprices"

# Maximum loops per optimization run (optional).
# If not specified, runs until all replay ticks are consumed.
# Example: max_loops = 50000

[parameter_space.strategy]
# Core CryptoPrices strategy parameters

# Pricing edge: how much the market must be underpriced vs theoretical
# UPDATED: Minimum increased from 2% to 10% to account for execution costs (2-3%)
# 0.10 = 10% edge required, 0.25 = 25% edge required
min_pricing_edge = { min = 0.10, max = 0.25 }

# Uncertain price zone: skip trades when market price falls in this range
# LOCKED: Standard range works well for successful strategies (SOL_15m, XRP_15m)
uncertain_zone_min = { min = 0.35, max = 0.35 }
uncertain_zone_max = { min = 0.65, max = 0.65 }

# Stop loss percentage (e.g., 0.30 = cut losses at 70% of position value)
# OPTIMIZED: Key parameter - testing 25-45% range for best risk management
stop_loss_pct = { min = 0.25, max = 0.45 }

# Trade cooldown (seconds to wait after exit before re-entering)
# LOCKED: 2-3 minute cooldown prevents overtrading (works for SOL_15m/XRP_15m)
trade_cooldown_ticks = { min = 150, max = 150 }

# Minimum time to expiry required to enter trade (seconds)
# LOCKED: 4 minutes gives enough time for trade thesis to develop on 15m events
min_time_to_expiry_seconds = { min = 240, max = 240 }

[parameter_space.global]
# Core module parameters
stop_loss_timeout = { min = 15, max = 15 }
stop_loss_recovery_buffer = { min = 0.03, max = 0.03 }

# Adaptive trailing stop parameters
# OPTIMIZED: Critical for profit capture
# Test different profit thresholds and trailing percentages
adaptive_high_profit_threshold = { min = 0.20, max = 0.35 }
adaptive_medium_profit_threshold = { min = 0.10, max = 0.20 }
adaptive_high_trailing_pct = { min = 0.75, max = 0.85 }
adaptive_medium_trailing_pct = { min = 0.75, max = 0.85 }
base_trailing_pct = { min = 0.65, max = 0.80 }

[objective]
# FIXED: "total_profit" can encourage overtrading (SOL_15m had 31 trades/hour!)
# Consider: "sharpe_ratio" for risk-adjusted returns or "profit_per_trade" for quality
# Available: total_profit, sharpe_ratio, profit_per_trade, win_rate, avg_profit_per_trade
function = "win_rate"

[pso]
# PSO algorithm parameters
swarm_size = 20
max_iterations = 50
inertia_weight = 0.729
cognitive_coefficient = 1.49445
social_coefficient = 1.49445