Learning System =============== The learning system enables agents to improve their skills through courses, unlocking better performance and new capabilities. Overview -------- Learning Model ~~~~~~~~~~~~~~ Agents can: * Enroll in courses to improve skills * Learn new trading strategies * Unlock advanced factors * Increase analysis accuracy Course Types ~~~~~~~~~~~~ * **Beginner Courses**: Basic trading concepts * **Intermediate Courses**: Technical analysis * **Advanced Courses**: Complex strategies * **Specialization Courses**: Specific asset classes Using the Learning System ------------------------- Browse Available Courses ~~~~~~~~~~~~~~~~~~~~~~~~ .. code-block:: python from openclaw.learning.manager import LearningManager # Create learning manager manager = LearningManager() # List all available courses courses = manager.list_courses() for course in courses: print(f"{course.name}: {course.price} credits") print(f" Duration: {course.duration_hours} hours") print(f" Skill gain: +{course.skill_boost:.0%}") Enroll in a Course ~~~~~~~~~~~~~~~~~~ .. code-block:: python from openclaw.learning.manager import LearningManager manager = LearningManager() # Enroll agent in a course if manager.can_enroll(agent, "technical_analysis_101"): enrollment = manager.enroll( agent=agent, course_id="technical_analysis_101" ) print(f"Enrolled: {enrollment.course.name}") else: print("Cannot enroll: insufficient funds or prerequisites not met") Complete a Course ~~~~~~~~~~~~~~~~~ .. code-block:: python # Complete the course result = manager.complete_course(agent, "technical_analysis_101") if result.success: print(f"Course completed!") print(f"Skill increase: +{result.skill_increase:.0%}") print(f"New skill level: {agent.state.skill_level:.2f}") # Check unlocked factors if result.unlocked_factors: print(f"Unlocked factors: {result.unlocked_factors}") else: print(f"Course failed: {result.reason}") Course Categories ----------------- Beginner Courses ~~~~~~~~~~~~~~~~ **Trading Fundamentals** * Duration: 10 hours * Cost: $50 * Skill boost: +0.05 * Prerequisites: None Topics: * Basic market concepts * Order types * Risk management basics * Position sizing **Technical Analysis Basics** * Duration: 15 hours * Cost: $75 * Skill boost: +0.08 * Prerequisites: Trading Fundamentals Topics: * Chart patterns * Support and resistance * Trend identification * Basic indicators Intermediate Courses ~~~~~~~~~~~~~~~~~~~~ **Advanced Technical Analysis** * Duration: 20 hours * Cost: $150 * Skill boost: +0.12 * Prerequisites: Technical Analysis Basics Topics: * Complex patterns * Multiple timeframe analysis * Advanced indicators * Volume analysis **Sentiment Analysis** * Duration: 18 hours * Cost: $125 * Skill boost: +0.10 * Prerequisites: Trading Fundamentals Topics: * News analysis * Social media sentiment * Market mood indicators * Contrarian strategies Advanced Courses ~~~~~~~~~~~~~~~~ **Algorithmic Trading** * Duration: 40 hours * Cost: $500 * Skill boost: +0.20 * Prerequisites: Advanced Technical Analysis Topics: * Strategy development * Backtesting * Optimization * Risk management **Machine Learning for Trading** * Duration: 50 hours * Cost: $750 * Skill boost: +0.25 * Prerequisites: Algorithmic Trading Topics: * Feature engineering * Model selection * Training and validation * Live deployment Specialization Courses ~~~~~~~~~~~~~~~~~~~~~~ **Forex Trading** * Duration: 25 hours * Cost: $300 * Skill boost: +0.15 (forex only) **Cryptocurrency Trading** * Duration: 20 hours * Cost: $250 * Skill boost: +0.12 (crypto only) **Options Trading** * Duration: 35 hours * Cost: $600 * Skill boost: +0.18 (options only) Learning Manager ---------------- Managing Enrollments ~~~~~~~~~~~~~~~~~~~~ .. code-block:: python from openclaw.learning.manager import LearningManager manager = LearningManager() # Get agent's active courses active = manager.get_active_courses(agent) for course in active: print(f"In progress: {course.name}") print(f" Progress: {course.progress:.0%}") print(f" Time remaining: {course.time_remaining} hours") # Pause a course manager.pause_course(agent, "technical_analysis_101") # Resume a course manager.resume_course(agent, "technical_analysis_101") Checking Prerequisites ~~~~~~~~~~~~~~~~~~~~~~ .. code-block:: python # Check if prerequisites are met can_enroll = manager.check_prerequisites( agent=agent, course_id="advanced_technical" ) if not can_enroll: missing = manager.get_missing_prerequisites(agent, "advanced_technical") print(f"Missing prerequisites: {missing}") Course Creation --------------- Creating Custom Courses ~~~~~~~~~~~~~~~~~~~~~~~ .. code-block:: python from openclaw.learning.courses import Course, CourseContent # Create course content content = CourseContent( modules=[ { "title": "Module 1: Introduction", "duration_hours": 2, "topics": ["Overview", "Setup"] }, { "title": "Module 2: Advanced Concepts", "duration_hours": 5, "topics": ["Strategy", "Implementation"] } ], assessments=[ { "type": "quiz", "passing_score": 0.8 }, { "type": "practical", "requirements": ["Complete 5 trades"] } ] ) # Create course course = Course( course_id="custom_strategy", name="Custom Strategy Development", description="Learn to develop custom trading strategies", duration_hours=20, price=200.0, skill_boost=0.15, prerequisites=["technical_analysis_basics"], unlocks_factors=["custom_factor_1", "custom_factor_2"], content=content ) # Register course manager.register_course(course) Learning Progression -------------------- Typical Learning Path ~~~~~~~~~~~~~~~~~~~~~ 1. **Start**: Skill level 0.5 2. **Beginner Courses**: Skill level 0.5 → 0.65 3. **Intermediate Courses**: Skill level 0.65 → 0.80 4. **Advanced Courses**: Skill level 0.80 → 0.95 5. **Specialization**: Skill level 0.95 → 1.0 Skill Level Benefits ~~~~~~~~~~~~~~~~~~~~ Higher skill levels provide: * More accurate analysis * Better trade timing * Lower error rates * Access to advanced factors * Improved win rates Learning vs Trading Balance --------------------------- When to Learn ~~~~~~~~~~~~~ .. code-block:: python from openclaw.core.work_trade_balance import WorkTradeBalance balance = WorkTradeBalance(agent) # Check if agent should learn if balance.should_focus_on_learning(): # Find appropriate course course = manager.recommend_course(agent) if course: manager.enroll(agent, course.id) print(f"Enrolled in {course.name} to improve skills") else: print("Agent should continue trading") Auto-Learning Mode ~~~~~~~~~~~~~~~~~~ .. code-block:: python # Enable auto-learning agent.enable_auto_learning( threshold=0.3, # Learn when balance drops below 30% max_course_cost=0.2 # Spend max 20% of balance on courses ) Learning Analytics ------------------ Track Learning Progress ~~~~~~~~~~~~~~~~~~~~~~~ .. code-block:: python # Get learning statistics stats = manager.get_learning_stats(agent) print(f"Courses completed: {stats.completed_courses}") print(f"Total learning hours: {stats.total_hours}") print(f"Total skill gain: +{stats.total_skill_gain:.0%}") print(f"Learning investment: ${stats.total_investment:.2f}") print(f"ROI: {stats.roi:.2f}x") Learning Recommendations ~~~~~~~~~~~~~~~~~~~~~~~~ .. code-block:: python # Get personalized recommendations recommendations = manager.recommend_courses(agent) print("Recommended courses:") for rec in recommendations: print(f" {rec.course.name}") print(f" Expected benefit: {rec.expected_benefit:.2f}") print(f" Priority: {rec.priority}")