القائمة
Centralized configuration management with validation, versioning, and environment-specific overrides for the ML Engine FX trading system.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Triggered on runtime errors in the scan loop. Diagnoses the root cause, writes a targeted fix to src/, and reports what changed. The calling harness runs pytest before merging — if tests fail, the fix is reverted automatically.
Post-trade feedback loop system: shaped-reward logging, diagnostics, and self-heal. Use when investigating trade outcomes, checking system health, or understanding automated corrective actions.
Buddy MCP server: 15 tools for system observability, feedback loop control, OANDA state, and knowledge management. Use when querying system health, investigating trades, or triggering diagnostics/self-heal.
Spawned automatically on trade close by ClaudeReflectionHandler. Reads the just-closed trade, compares prediction to outcome, and writes structured learnings / rule drafts / weight proposals that Buddy picks up on the next scan cycle.
Triggered on runtime errors in the scan loop. Diagnoses the root cause, writes a targeted fix to src/, and reports what changed. The calling harness runs pytest before merging — if tests fail, the fix is reverted automatically.
Spawned automatically on trade close by ClaudeReflectionHandler. Reads the just-closed trade, compares prediction to outcome, and writes structured learnings / rule drafts / weight proposals that Buddy picks up on the next scan cycle.
| name | ML Engine Configuration Manager |
| version | 1.0.0 |
| author | Kilo Code |
| description | Centralized configuration management with validation, versioning, and environment-specific overrides for the ML Engine FX trading system. |
| tags | ["configuration","validation","yaml","environment","versioning"] |
| category | infrastructure |
| dependencies | [] |
Centralized configuration management system for the ML Engine FX trading ecosystem with validation, environment-specific overrides, versioning, and automated migration.
The Configuration Manager skill provides robust configuration handling for the ML Engine's complex multi-model ensemble system. It addresses configuration drift, validation gaps, and deployment complexity across different environments (development, testing, production).
# Validate a configuration file
python -m kilocode.skills.ml_engine_config_manager validate config/config_improved_H1.yaml
# Validate with detailed output
python -m kilocode.skills.ml_engine_config_manager validate config/config_improved_H1.yaml --verbose
# Check for deprecated fields
python -m kilocode.skills.ml_engine_config_manager validate config/config_improved_H1.yaml --check-deprecated
# Generate from default template
python -m kilocode.skills.ml_engine_config_manager generate --output config/my_config.yaml
# Generate for specific pair
python -m kilocode.skills.ml_engine_config_manager generate --pair EUR_USD --output config/eur_usd_config.yaml
# Generate with custom settings
python -m kilocode.skills.ml_engine_config_manager generate \
--pair GBP_JPY \
--seq-len 120 \
--epochs 200 \
--output config/gbp_jpy_config.yaml
# Apply production overrides
python -m kilocode.skills.ml_engine_config_manager override \
--base config/config_improved_H1.yaml \
--env production \
--output config/production_config.yaml
# Apply custom overrides
python -m kilocode.skills.ml_engine_config_manager override \
--base config/config_improved_H1.yaml \
--env custom \
--overrides '{"training.epochs": 200, "training.batch_size": 128}' \
--output config/custom_config.yaml
# Visual diff between two configs
python -m kilocode.skills.ml_engine_config_manager diff \
config/config_improved_H1.yaml \
config/config_m1_optimized.yaml
# Diff with color output
python -m kilocode.skills.ml_engine_config_manager diff \
config/config_improved_H1.yaml \
config/config_m1_optimized.yaml \
--color
# Diff with structural comparison (ignoring values)
python -m kilocode.skills.ml_engine_config_manager diff \
config/config_improved_H1.yaml \
config/config_m1_optimized.yaml \
--structure-only
# Migrate to latest schema version
python -m kilocode.skills.ml_engine_config_manager migrate \
config/legacy_config.yaml \
--output config/migrated_config.yaml
# Migrate with dry-run (preview changes)
python -m kilocode.skills.ml_engine_config_manager migrate \
config/legacy_config.yaml \
--output config/migrated_config.yaml \
--dry-run
# Migrate with backup
python -m kilocode.skills.ml_engine_config_manager migrate \
config/legacy_config.yaml \
--output config/migrated_config.yaml \
--backup
The ML Engine configuration follows a hierarchical structure with these main sections:
CLI and command interface settings
buddy:
fetch_days: 365 # Days of historical data to fetch
fetch_candles: 3000 # Number of candles for training
instrument: "EUR_USD" # Default trading pair
Training pipeline configuration
training:
epochs: 100 # Training epochs
batch_size: 64 # Batch size
learning_rate: 0.001 # Learning rate
patience: 20 # Early stopping patience
seq_len: 60 # Sequence length for sliding window
use_class_balanced_loss: true # Use class-balanced loss
use_anti_collapse_loss: true # Use anti-collapse loss
Model architecture settings
model:
transformer_d_model: 32 # Transformer model dimension
transformer_num_heads: 4 # Number of attention heads
transformer_num_layers: 2 # Number of encoder layers
transformer_dff: 64 # Feedforward network dimension
transformer_dropout: 0.2 # Dropout rate
Data processing and feature engineering
data:
use_normalized_features: true # Use normalized features
feature_engineering: # Feature engineering settings
adx_period: 14
rsi_period: 14
macd_fast: 12
macd_slow: 26
FX trading settings and guardrails
fx:
session_windows: # Trading session windows
london: ["08:00", "11:30"]
new_york: ["13:00", "17:00"]
daily_circuit_breaker: # Daily loss limit
enabled: true
max_loss_pips: 100
spread_filter: # Spread filter
enabled: true
max_spread_pips: 3.0
# config/overrides/dev.yaml
training:
epochs: 10 # Quick iteration
batch_size: 32 # Smaller batches
model:
transformer_d_model: 16 # Smaller model
fx:
paper_trading: true # Always paper trade in dev
# config/overrides/test.yaml
training:
epochs: 20
use_ema: false # Disable for speed
use_ewc: false # Disable for speed
data:
fetch_days: 90 # Smaller dataset
# config/overrides/prod.yaml
training:
epochs: 100
patience: 20
use_class_balanced_loss: true
use_anti_collapse_loss: true
use_ema: true
use_ewc: true
fx:
paper_trading: false
session_windows:
london: ["08:00", "11:30"]
new_york: ["13:00", "17:00"]
daily_circuit_breaker:
enabled: true
max_loss_pips: 100
Create custom validation rules in references/custom_validators.py:
from pydantic import validator
class CustomConfig(BaseModel):
# Custom validator for sequence length
@validator('seq_len')
def validate_seq_len(cls, v):
if v < 30:
raise ValueError('seq_len must be at least 30')
if v > 200:
raise ValueError('seq_len cannot exceed 200')
if v % 10 != 0:
raise ValueError('seq_len must be multiple of 10')
return v
Use templates in references/config_templates/ for common scenarios:
# List available templates
python -m kilocode.skills.ml_engine_config_manager list-templates
# Generate from template
python -m kilocode.skills.ml_engine_config_manager generate \
--template aggressive \
--pair GBP_JPY \
--output config/aggressive_gbp_jpy.yaml
# Analyze differences between configs
python -m kilocode.skills.ml_engine_config_manager analyze-diff \
config/config_improved_H1.yaml \
config/config_m1_optimized.yaml \
--output reports/config_diff_analysis.md
# Include impact analysis
python -m kilocode.skills.ml_engine_config_manager analyze-diff \
config/config_improved_H1.yaml \
config/config_m1_optimized.yaml \
--impact \
--output reports/config_impact_analysis.md
# Create backup
python -m kilocode.skills.ml_engine_config_manager backup \
config/config_improved_H1.yaml \
--output backups/config_backup_20240129.yaml
# List backups
python -m kilocode.skills.ml_engine_config_manager list-backups
# Restore from backup
python -m kilocode.skills.ml_engine_config_manager restore \
backups/config_backup_20240129.yaml \
--output config/config_improved_H1.yaml
The Configuration Manager provides clear error messages for common issues:
ValidationError: training.epochs must be at least 10
ValidationError: model.transformer_d_model must be multiple of 8
ValidationError: fx.session_windows.london must be in format HH:MM
MigrationError: Cannot migrate from version 1.0 to 3.0 (unsupported)
MigrationError: Field 'legacy_field' is deprecated and has no migration path
OverrideError: Override path 'training.invalid_field' does not exist in base config
OverrideError: Override type mismatch: expected int, got str
Solution: Use --verbose flag to see detailed validation context
python -m kilocode.skills.ml_engine_config_manager validate config.yaml --verbose
Solution: Use --dry-run to preview migration changes
python -m kilocode.skills.ml_engine_config_manager migrate old.yaml --dry-run
Solution: Check override path matches base config structure exactly
# Check structure first
python -m kilocode.skills.ml_engine_config_manager show-structure config.yaml
The Configuration Manager integrates seamlessly with ML Engine:
# In main.py
from kilocode.skills.ml_engine_config_manager import ConfigManager
# Load and validate config
config = ConfigManager.load('config/config_improved_H1.yaml')
# Access config with type hints
epochs = config.training.epochs
seq_len = config.data.seq_len
# Apply environment overrides
config = ConfigManager.apply_overrides(
config,
environment='production'
)
# Validate before training
errors = ConfigManager.validate(config)
if errors:
print(f"Configuration errors: {errors}")
sys.exit(1)